Machine Learning 第八波编程作业（完）——Anomaly Detection and Recommender Systems

仅列出核心代码：

1.estimateGuassian.m

mu = mean(X)';
X2 = (X - ones(m, 1)*mu').^2;
sigma2 = mean(X2);

2.selectThreshold.m

cvPredictions = (pval < epsilon);
tp = sum((cvPredictions == 1) & (yval == 1));
fp = sum((cvPredictions == 1) & (yval == 0));
fn = sum((cvPredictions == 0) & (yval == 1));
prec = tp/(tp + fp);
rec = tp/(tp + fn);
F1 = 2*prec*rec/(prec + rec);

3.cofiCostFunc.m

X1 = (X*Theta'- Y).*R;
reg1 = (sum(sum(X.^2)) + sum(sum(Theta.^2)))*lambda/2;
J = sum(sum((X1).^2))/2 + reg1;

X_grad = X1*Theta + lambda*X;
Theta_grad = X1'*X + lambda*Theta;

课程地址：https://www.coursera.org/course/ml

 

Machine Learning 第七波编程作业——K-means Clustering and Principal Component Analysis

仅列出核心代码：

1.findClosestCentroids.m

m = size(X, 1);
len = zeros(K, 1);
for i = 1:m
    for j = 1:K
        len(j) = norm(X(i, :) - centroids(j, :))^2;
    end
    [~, idx(i)] = min(len);
end

2.computeCentroids.m

for k = 1:K
    ind = find(idx == k);
    centroids(k, :) = mean(X(ind, :));
end

3.pca.m

Sigma = X'*X/m;
[U,S,~] = svd(Sigma);

4.projectData.m

Z = X * U(:, 1:K);

5.recoverData.m

X_rec = Z * U(:, 1:K)';

课程地址：https://www.coursera.org/course/ml

Machine Learning 第六波编程作业——Support Vector Machines

仅列出核心代码：

1.gaussianKernel.m

sim = exp(-sum((x1 - x2).^2) /(2*sigma^2));

2.dataset3Params.m

TD =  [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30];
pre_err = zeros(length(TD));
for i = 1:length(TD)
    for j = 1:length(TD)
        C = TD(i);
        sigma = TD(j);
        model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
        predictions = svmPredict(model, Xval);
        pre_err(i, j) = mean(double(predictions ~= yval));
    end
end
mm = min(min(pre_err));
[ind_C, ind_sigma] = find(pre_err == mm);
C = TD(ind_C);
sigma = TD(ind_sigma);

3.processEmail.m

for i = 1:length(vocabList)
v = strcmp(str, vocabList(i));
    if v==1
        word_indices = [word_indices ; i];
    end
end

4.emailFeatures.m

x(word_indices) = 1;

课程地址：https://www.coursera.org/course/ml

Machine Learning 第五波编程作业 – Regularized Linear Regression and Bias/Variance

仅列出核心代码：

1.linearRegCostFunction.m

h = X * theta;
J = (X * theta - y).' * (X * theta - y) / (2*m)...
    +(lambda/(2*m)) * sum(theta(2:end).^2);
grad = grad(:);
grad(1) = (X(:, 1).' * (h - y)) /m;

grad(2:end) = (X(:, 2:end).' * (h - y)) /m ...
+ (lambda/m) * theta(2:end);

2.learningCurve.m

for i = 1:m
    Xi = X(1:i, :);
    yi = y(1:i);
    lambda = 1;
    [theta] = trainLinearReg(Xi, yi, lambda);
    lambda = 0;
    % For train error, make sure you compute it on the training subset
    [error_train(i), ~] = linearRegCostFunction(Xi, yi, theta, lambda);
    % For validation error, compute it over the entire cross validation set
    [error_val(i), ~] = linearRegCostFunction(Xval, yval, theta, lambda);
end

3.polyFeatures.m

for i =1:p
    X_poly(:, i) = X(:, 1).^i;
end

4.validationCurve.m

for i = 1:length(lambda_vec)
    [theta] = trainLinearReg(X, y, lambda_vec(i));
    % For train error, make sure you compute it on the training subset
    [error_train(i), ~] = linearRegCostFunction(X, y, theta, 0);
    % For validation error, compute it over the entire cross validation set
    [error_val(i), ~] = linearRegCostFunction(Xval, yval, theta, 0);
end

课程地址：https://www.coursera.org/course/ml

Machine Learning 第四波编程作业 - Neural Networks: Learning

仅列出核心代码：

1.sigmoidGradient.m

h = 1.0 ./ (1.0 + exp(-z));
g = h.*(1 - h);

2.randInitializeWeights.m

epsilon_init = 0.12;
W = rand(L_out, 1 + L_in)*2*epsilon_init - epsilon_init;

3.nnCostFunction.m

% cost function

A1 = X;
A1 = [ones(m, 1), A1];
Z2 = A1 * Theta1.';
A2 = sigmoid(Z2);
A2 = [ones(m, 1), A2];
Z3 = A2 * Theta2.';
A3 = sigmoid(Z3);
H = A3;
Y = zeros(m, num_labels);
for ind = 1:m
Y(ind, y(ind)) = 1;
end
K = num_labels;
Jk = zeros(K, 1);

for k =1:K

Jk(k) = ( -Y(:, k).' *log(H(:, k)) )-( (1 - Y(:, k)).' * log(1-H(:, k)) );

end
J = sum(Jk)/m;

J = J + ( lambda/(2*m) )*( sum(sum(Theta1(:, 2:end).^2))+sum(sum(Theta2(:, 2:end).^2)) );

% Unroll gradients

delta3 = A3 - Y;
delta2 = delta3*Theta2.* (A2.*(1-A2));
delta2 = delta2(:, 2: end);
Delta2 = zeros(size(delta3, 2), size(A2, 2));
Delta1 = zeros(size(delta2, 2), size(A1, 2));
for i=1:m
Delta2 = Delta2 + delta3(i, :).' * A2(i, :);
Delta1 = Delta1 + delta2(i, :).' * A1(i, :);
end
Theta1_grad = Delta1/m;
Theta1_grad(:, 2:end) = Theta1_grad(:, 2:end) + Theta1(:, 2:end)*(lambda/m);
Theta2_grad = Delta2/m;
Theta2_grad(:, 2:end) = Theta2_grad(:, 2:end) + Theta2(:, 2:end)*(lambda/m);
grad = [Theta1_grad(:) ; Theta2_grad(:)];
end

课程地址：https://www.coursera.org/course/ml

Machine Learning 第三波编程作业 – Multi-class Classification and Neural Networks

仅列出核心代码：

1.lrCostFunction.m

h = sigmoid(X * theta);   %   h_theta(X) : m*1
%   Cost func
J = (-log(h.')*y - log(ones(1, m) - h.')*(ones(m, 1) - y)) / m ...
    +(lambda/(2*m)) * sum(theta(2:end).^2);

%   Gradient
grad(1) = (X(:, 1).' * (h - y)) /m;

grad(2:end) = (X(:, 2:end).' * (h - y)) /m ...
    + (lambda/m) * theta(2:end);

2.oneVsAll.m

options = optimset('GradObj', 'on', 'MaxIter', 50);
initial_theta = zeros(size(X, 2), 1);
for c = 1:num_labels
    [all_theta(c, :)] = fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)),...
        initial_theta, options);   
end

3.predictOneVsAll.m

[~, p] = max(X * all_theta.', [], 2);

4.predict.m

X = [ones(size(X), 1), X]; % Add ones to the X data matrix

X1 = sigmoid(X * Theta1.');
X1 = [ones(size(X1), 1), X1]; % Add ones to the X1 data matrix

[~, p] = max(X1 * Theta2.', [], 2);

课程地址：https://www.coursera.org/course/ml

Machine Learning 第二波编程作业 – Logistic Regression

仅列出核心代码：

1.plotData.m

ind1 = find(y==1); ind0 = find(y==0);
plot(X(ind1, 1), X(ind1, 2), 'k+','LineWidth', 2, 'MarkerSize', 7);
plot(X(ind0, 1), X(ind0, 2), 'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7);

2.sigmoid.m

g = 1 ./ (ones(size(z)) + exp(-z));

3.costFunction.m

h = sigmoid(X * theta); % h_theta(X) : m*1
J = (-log(h.')*y - log(ones(1, m) - h.')*(ones(m, 1) - y)) / m;
grad = (X.' * (h - y)) /m;

4.predict.m

h = sigmoid(X * theta);
p = (h >= 0.5);

5.costFunctionReg.m

h = sigmoid(X * theta); % h_theta(X) : m*1
% Cost func
J = (-log(h.')*y - log(ones(1, m) - h.')*(ones(m, 1) - y)) / m ...
+(lambda/(2*m)) * sum(theta(2:end).^2);

% Gradient
grad(1) = (X(:, 1).' * (h - y)) /m;

grad(2:end) = (X(:, 2:end).' * (h - y)) /m ...
+ (lambda/m) * theta(2:end);

课程地址：https://www.coursera.org/course/ml

Machine Learning 第一波编程作业 - Linear Regression

仅列出核心代码：

1.computeCost

J = sum((X*theta-y).^2)/(2*m);

2.gradientDescent

theta = theta - (1/m)*alpha*(X.'*(X*theta-y));

3.featureNormalize

mu = mean(X);
sigma = std(X);
X_norm = (X - ones(size(X, 1), 1) * mu) ./ (ones(size(X, 1), 1) * sigma);

4.computeCostMulti

J = (X * theta - y).' * (X * theta - y) / (2*m);

5.gradientDescentMulti

theta = theta - (1/m)*alpha*(X.'*(X*theta-y));

6.normalEqn

theta = inv(X.' * X) * X.' * y;

课程地址：https://www.coursera.org/course/ml