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Ning Sun  committed 72f9a77

add ex8

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File mlclass-ex8/checkCostFunction.m

+function checkCostFunction(lambda)
+%CHECKCOSTFUNCTION Creates a collaborative filering problem 
+%to check your cost function and gradients
+%   CHECKCOSTFUNCTION(lambda) Creates a collaborative filering problem 
+%   to check your cost function and gradients, it will output the 
+%   analytical gradients produced by your code and the numerical gradients 
+%   (computed using computeNumericalGradient). These two gradient 
+%   computations should result in very similar values.
+
+% Set lambda
+if ~exist('lambda', 'var') || isempty(lambda)
+    lambda = 0;
+end
+
+%% Create small problem
+X_t = rand(4, 3);
+Theta_t = rand(5, 3);
+
+% Zap out most entries
+Y = X_t * Theta_t';
+Y(rand(size(Y)) > 0.5) = 0;
+R = zeros(size(Y));
+R(Y ~= 0) = 1;
+
+%% Run Gradient Checking
+X = randn(size(X_t));
+Theta = randn(size(Theta_t));
+num_users = size(Y, 2);
+num_movies = size(Y, 1);
+num_features = size(Theta_t, 2);
+
+numgrad = computeNumericalGradient( ...
+                @(t) cofiCostFunc(t, Y, R, num_users, num_movies, ...
+                                num_features, lambda), [X(:); Theta(:)]);
+
+[cost, grad] = cofiCostFunc([X(:); Theta(:)],  Y, R, num_users, ...
+                          num_movies, num_features, lambda);
+
+disp([numgrad grad]);
+fprintf(['The above two columns you get should be very similar.\n' ...
+         '(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']);
+
+diff = norm(numgrad-grad)/norm(numgrad+grad);
+fprintf(['If your backpropagation implementation is correct, then \n' ...
+         'the relative difference will be small (less than 1e-9). \n' ...
+         '\nRelative Difference: %g\n'], diff);
+
+end

File mlclass-ex8/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
+%
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+% =============================================================
+
+grad = [X_grad(:); Theta_grad(:)];
+
+end

File mlclass-ex8/computeNumericalGradient.m

+function numgrad = computeNumericalGradient(J, theta)
+%COMPUTENUMERICALGRADIENT Computes the gradient using "finite differences"
+%and gives us a numerical estimate of the gradient.
+%   numgrad = COMPUTENUMERICALGRADIENT(J, theta) computes the numerical
+%   gradient of the function J around theta. Calling y = J(theta) should
+%   return the function value at theta.
+
+% Notes: The following code implements numerical gradient checking, and 
+%        returns the numerical gradient.It sets numgrad(i) to (a numerical 
+%        approximation of) the partial derivative of J with respect to the 
+%        i-th input argument, evaluated at theta. (i.e., numgrad(i) should 
+%        be the (approximately) the partial derivative of J with respect 
+%        to theta(i).)
+%                
+
+numgrad = zeros(size(theta));
+perturb = zeros(size(theta));
+e = 1e-4;
+for p = 1:numel(theta)
+    % Set perturbation vector
+    perturb(p) = e;
+    loss1 = J(theta - perturb);
+    loss2 = J(theta + perturb);
+    % Compute Numerical Gradient
+    numgrad(p) = (loss2 - loss1) / (2*e);
+    perturb(p) = 0;
+end
+
+end

File mlclass-ex8/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.
+%
+
+
+
+
+
+
+
+
+
+
+% =============================================================
+
+
+end

File mlclass-ex8/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 off
+
+fprintf('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
+
+
+

File mlclass-ex8/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
+end
+
+fprintf('\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=1:10
+    j = ix(i);
+    fprintf('Predicting rating %.1f for movie %s\n', my_predictions(j), ...
+            movieList{j});
+end
+
+fprintf('\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

File mlclass-ex8/ex8_movieParams.mat

Binary file added.

File mlclass-ex8/ex8_movies.mat

Binary file added.

File mlclass-ex8/ex8data1.mat

Binary file added.

File mlclass-ex8/ex8data2.mat

Binary file added.

File mlclass-ex8/fmincg.m

+function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
+% Minimize a continuous differentialble multivariate function. Starting point
+% is given by "X" (D by 1), and the function named in the string "f", must
+% return a function value and a vector of partial derivatives. The Polack-
+% Ribiere flavour of conjugate gradients is used to compute search directions,
+% and a line search using quadratic and cubic polynomial approximations and the
+% Wolfe-Powell stopping criteria is used together with the slope ratio method
+% for guessing initial step sizes. Additionally a bunch of checks are made to
+% make sure that exploration is taking place and that extrapolation will not
+% be unboundedly large. The "length" gives the length of the run: if it is
+% positive, it gives the maximum number of line searches, if negative its
+% absolute gives the maximum allowed number of function evaluations. You can
+% (optionally) give "length" a second component, which will indicate the
+% reduction in function value to be expected in the first line-search (defaults
+% to 1.0). The function returns when either its length is up, or if no further
+% progress can be made (ie, we are at a minimum, or so close that due to
+% numerical problems, we cannot get any closer). If the function terminates
+% within a few iterations, it could be an indication that the function value
+% and derivatives are not consistent (ie, there may be a bug in the
+% implementation of your "f" function). The function returns the found
+% solution "X", a vector of function values "fX" indicating the progress made
+% and "i" the number of iterations (line searches or function evaluations,
+% depending on the sign of "length") used.
+%
+% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
+%
+% See also: checkgrad 
+%
+% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13
+%
+%
+% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen
+% 
+% Permission is granted for anyone to copy, use, or modify these
+% programs and accompanying documents for purposes of research or
+% education, provided this copyright notice is retained, and note is
+% made of any changes that have been made.
+% 
+% These programs and documents are distributed without any warranty,
+% express or implied.  As the programs were written for research
+% purposes only, they have not been tested to the degree that would be
+% advisable in any important application.  All use of these programs is
+% entirely at the user's own risk.
+%
+% [ml-class] Changes Made:
+% 1) Function name and argument specifications
+% 2) Output display
+%
+
+% Read options
+if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter')
+    length = options.MaxIter;
+else
+    length = 100;
+end
+
+
+RHO = 0.01;                            % a bunch of constants for line searches
+SIG = 0.5;       % RHO and SIG are the constants in the Wolfe-Powell conditions
+INT = 0.1;    % don't reevaluate within 0.1 of the limit of the current bracket
+EXT = 3.0;                    % extrapolate maximum 3 times the current bracket
+MAX = 20;                         % max 20 function evaluations per line search
+RATIO = 100;                                      % maximum allowed slope ratio
+
+argstr = ['feval(f, X'];                      % compose string used to call function
+for i = 1:(nargin - 3)
+  argstr = [argstr, ',P', int2str(i)];
+end
+argstr = [argstr, ')'];
+
+if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end
+S=['Iteration '];
+
+i = 0;                                            % zero the run length counter
+ls_failed = 0;                             % no previous line search has failed
+fX = [];
+[f1 df1] = eval(argstr);                      % get function value and gradient
+i = i + (length<0);                                            % count epochs?!
+s = -df1;                                        % search direction is steepest
+d1 = -s'*s;                                                 % this is the slope
+z1 = red/(1-d1);                                  % initial step is red/(|s|+1)
+
+while i < abs(length)                                      % while not finished
+  i = i + (length>0);                                      % count iterations?!
+
+  X0 = X; f0 = f1; df0 = df1;                   % make a copy of current values
+  X = X + z1*s;                                             % begin line search
+  [f2 df2] = eval(argstr);
+  i = i + (length<0);                                          % count epochs?!
+  d2 = df2'*s;
+  f3 = f1; d3 = d1; z3 = -z1;             % initialize point 3 equal to point 1
+  if length>0, M = MAX; else M = min(MAX, -length-i); end
+  success = 0; limit = -1;                     % initialize quanteties
+  while 1
+    while ((f2 > f1+z1*RHO*d1) | (d2 > -SIG*d1)) & (M > 0) 
+      limit = z1;                                         % tighten the bracket
+      if f2 > f1
+        z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3);                 % quadratic fit
+      else
+        A = 6*(f2-f3)/z3+3*(d2+d3);                                 % cubic fit
+        B = 3*(f3-f2)-z3*(d3+2*d2);
+        z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A;       % numerical error possible - ok!
+      end
+      if isnan(z2) | isinf(z2)
+        z2 = z3/2;                  % if we had a numerical problem then bisect
+      end
+      z2 = max(min(z2, INT*z3),(1-INT)*z3);  % don't accept too close to limits
+      z1 = z1 + z2;                                           % update the step
+      X = X + z2*s;
+      [f2 df2] = eval(argstr);
+      M = M - 1; i = i + (length<0);                           % count epochs?!
+      d2 = df2'*s;
+      z3 = z3-z2;                    % z3 is now relative to the location of z2
+    end
+    if f2 > f1+z1*RHO*d1 | d2 > -SIG*d1
+      break;                                                % this is a failure
+    elseif d2 > SIG*d1
+      success = 1; break;                                             % success
+    elseif M == 0
+      break;                                                          % failure
+    end
+    A = 6*(f2-f3)/z3+3*(d2+d3);                      % make cubic extrapolation
+    B = 3*(f3-f2)-z3*(d3+2*d2);
+    z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3));        % num. error possible - ok!
+    if ~isreal(z2) | isnan(z2) | isinf(z2) | z2 < 0   % num prob or wrong sign?
+      if limit < -0.5                               % if we have no upper limit
+        z2 = z1 * (EXT-1);                 % the extrapolate the maximum amount
+      else
+        z2 = (limit-z1)/2;                                   % otherwise bisect
+      end
+    elseif (limit > -0.5) & (z2+z1 > limit)          % extraplation beyond max?
+      z2 = (limit-z1)/2;                                               % bisect
+    elseif (limit < -0.5) & (z2+z1 > z1*EXT)       % extrapolation beyond limit
+      z2 = z1*(EXT-1.0);                           % set to extrapolation limit
+    elseif z2 < -z3*INT
+      z2 = -z3*INT;
+    elseif (limit > -0.5) & (z2 < (limit-z1)*(1.0-INT))   % too close to limit?
+      z2 = (limit-z1)*(1.0-INT);
+    end
+    f3 = f2; d3 = d2; z3 = -z2;                  % set point 3 equal to point 2
+    z1 = z1 + z2; X = X + z2*s;                      % update current estimates
+    [f2 df2] = eval(argstr);
+    M = M - 1; i = i + (length<0);                             % count epochs?!
+    d2 = df2'*s;
+  end                                                      % end of line search
+
+  if success                                         % if line search succeeded
+    f1 = f2; fX = [fX' f1]';
+    fprintf('%s %4i | Cost: %4.6e\r', S, i, f1);
+    s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2;      % Polack-Ribiere direction
+    tmp = df1; df1 = df2; df2 = tmp;                         % swap derivatives
+    d2 = df1'*s;
+    if d2 > 0                                      % new slope must be negative
+      s = -df1;                              % otherwise use steepest direction
+      d2 = -s'*s;    
+    end
+    z1 = z1 * min(RATIO, d1/(d2-realmin));          % slope ratio but max RATIO
+    d1 = d2;
+    ls_failed = 0;                              % this line search did not fail
+  else
+    X = X0; f1 = f0; df1 = df0;  % restore point from before failed line search
+    if ls_failed | i > abs(length)          % line search failed twice in a row
+      break;                             % or we ran out of time, so we give up
+    end
+    tmp = df1; df1 = df2; df2 = tmp;                         % swap derivatives
+    s = -df1;                                                    % try steepest
+    d1 = -s'*s;
+    z1 = 1/(1-d1);                     
+    ls_failed = 1;                                    % this line search failed
+  end
+  if exist('OCTAVE_VERSION')
+    fflush(stdout);
+  end
+end
+fprintf('\n');

File mlclass-ex8/loadMovieList.m

+function movieList = loadMovieList()
+%GETMOVIELIST reads the fixed movie list in movie.txt and returns a
+%cell array of the words
+%   movieList = GETMOVIELIST() reads the fixed movie list in movie.txt 
+%   and returns a cell array of the words in movieList.
+
+
+%% Read the fixed movieulary list
+fid = fopen('movie_ids.txt');
+
+% Store all movies in cell array movie{}
+n = 1682;  % Total number of movies 
+
+movieList = cell(n, 1);
+for i = 1:n
+    % Read line
+    line = fgets(fid);
+    % Word Index (can ignore since it will be = i)
+    [idx, movieName] = strtok(line, ' ');
+    % Actual Word
+    movieList{i} = strtrim(movieName);
+end
+fclose(fid);
+
+end

File mlclass-ex8/movie_ids.txt

+1 Toy Story (1995)
+2 GoldenEye (1995)
+3 Four Rooms (1995)
+4 Get Shorty (1995)
+5 Copycat (1995)
+6 Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)
+7 Twelve Monkeys (1995)
+8 Babe (1995)
+9 Dead Man Walking (1995)
+10 Richard III (1995)
+11 Seven (Se7en) (1995)
+12 Usual Suspects, The (1995)
+13 Mighty Aphrodite (1995)
+14 Postino, Il (1994)
+15 Mr. Holland's Opus (1995)
+16 French Twist (Gazon maudit) (1995)
+17 From Dusk Till Dawn (1996)
+18 White Balloon, The (1995)
+19 Antonia's Line (1995)
+20 Angels and Insects (1995)
+21 Muppet Treasure Island (1996)
+22 Braveheart (1995)
+23 Taxi Driver (1976)
+24 Rumble in the Bronx (1995)
+25 Birdcage, The (1996)
+26 Brothers McMullen, The (1995)
+27 Bad Boys (1995)
+28 Apollo 13 (1995)
+29 Batman Forever (1995)
+30 Belle de jour (1967)
+31 Crimson Tide (1995)
+32 Crumb (1994)
+33 Desperado (1995)
+34 Doom Generation, The (1995)
+35 Free Willy 2: The Adventure Home (1995)
+36 Mad Love (1995)
+37 Nadja (1994)
+38 Net, The (1995)
+39 Strange Days (1995)
+40 To Wong Foo, Thanks for Everything! Julie Newmar (1995)
+41 Billy Madison (1995)
+42 Clerks (1994)
+43 Disclosure (1994)
+44 Dolores Claiborne (1994)
+45 Eat Drink Man Woman (1994)
+46 Exotica (1994)
+47 Ed Wood (1994)
+48 Hoop Dreams (1994)
+49 I.Q. (1994)
+50 Star Wars (1977)
+51 Legends of the Fall (1994)
+52 Madness of King George, The (1994)
+53 Natural Born Killers (1994)
+54 Outbreak (1995)
+55 Professional, The (1994)
+56 Pulp Fiction (1994)
+57 Priest (1994)
+58 Quiz Show (1994)
+59 Three Colors: Red (1994)
+60 Three Colors: Blue (1993)
+61 Three Colors: White (1994)
+62 Stargate (1994)
+63 Santa Clause, The (1994)
+64 Shawshank Redemption, The (1994)
+65 What's Eating Gilbert Grape (1993)
+66 While You Were Sleeping (1995)
+67 Ace Ventura: Pet Detective (1994)
+68 Crow, The (1994)
+69 Forrest Gump (1994)
+70 Four Weddings and a Funeral (1994)
+71 Lion King, The (1994)
+72 Mask, The (1994)
+73 Maverick (1994)
+74 Faster Pussycat! Kill! Kill! (1965)
+75 Brother Minister: The Assassination of Malcolm X (1994)
+76 Carlito's Way (1993)
+77 Firm, The (1993)
+78 Free Willy (1993)
+79 Fugitive, The (1993)
+80 Hot Shots! Part Deux (1993)
+81 Hudsucker Proxy, The (1994)
+82 Jurassic Park (1993)
+83 Much Ado About Nothing (1993)
+84 Robert A. Heinlein's The Puppet Masters (1994)
+85 Ref, The (1994)
+86 Remains of the Day, The (1993)
+87 Searching for Bobby Fischer (1993)
+88 Sleepless in Seattle (1993)
+89 Blade Runner (1982)
+90 So I Married an Axe Murderer (1993)
+91 Nightmare Before Christmas, The (1993)
+92 True Romance (1993)
+93 Welcome to the Dollhouse (1995)
+94 Home Alone (1990)
+95 Aladdin (1992)
+96 Terminator 2: Judgment Day (1991)
+97 Dances with Wolves (1990)
+98 Silence of the Lambs, The (1991)
+99 Snow White and the Seven Dwarfs (1937)
+100 Fargo (1996)
+101 Heavy Metal (1981)
+102 Aristocats, The (1970)
+103 All Dogs Go to Heaven 2 (1996)
+104 Theodore Rex (1995)
+105 Sgt. Bilko (1996)
+106 Diabolique (1996)
+107 Moll Flanders (1996)
+108 Kids in the Hall: Brain Candy (1996)
+109 Mystery Science Theater 3000: The Movie (1996)
+110 Operation Dumbo Drop (1995)
+111 Truth About Cats & Dogs, The (1996)
+112 Flipper (1996)
+113 Horseman on the Roof, The (Hussard sur le toit, Le) (1995)
+114 Wallace & Gromit: The Best of Aardman Animation (1996)
+115 Haunted World of Edward D. Wood Jr., The (1995)
+116 Cold Comfort Farm (1995)
+117 Rock, The (1996)
+118 Twister (1996)
+119 Maya Lin: A Strong Clear Vision (1994)
+120 Striptease (1996)
+121 Independence Day (ID4) (1996)
+122 Cable Guy, The (1996)
+123 Frighteners, The (1996)
+124 Lone Star (1996)
+125 Phenomenon (1996)
+126 Spitfire Grill, The (1996)
+127 Godfather, The (1972)
+128 Supercop (1992)
+129 Bound (1996)
+130 Kansas City (1996)
+131 Breakfast at Tiffany's (1961)
+132 Wizard of Oz, The (1939)
+133 Gone with the Wind (1939)
+134 Citizen Kane (1941)
+135 2001: A Space Odyssey (1968)
+136 Mr. Smith Goes to Washington (1939)
+137 Big Night (1996)
+138 D3: The Mighty Ducks (1996)
+139 Love Bug, The (1969)
+140 Homeward Bound: The Incredible Journey (1993)
+141 20,000 Leagues Under the Sea (1954)
+142 Bedknobs and Broomsticks (1971)
+143 Sound of Music, The (1965)
+144 Die Hard (1988)
+145 Lawnmower Man, The (1992)
+146 Unhook the Stars (1996)
+147 Long Kiss Goodnight, The (1996)
+148 Ghost and the Darkness, The (1996)
+149 Jude (1996)
+150 Swingers (1996)
+151 Willy Wonka and the Chocolate Factory (1971)
+152 Sleeper (1973)
+153 Fish Called Wanda, A (1988)
+154 Monty Python's Life of Brian (1979)
+155 Dirty Dancing (1987)
+156 Reservoir Dogs (1992)
+157 Platoon (1986)
+158 Weekend at Bernie's (1989)
+159 Basic Instinct (1992)
+160 Glengarry Glen Ross (1992)
+161 Top Gun (1986)
+162 On Golden Pond (1981)
+163 Return of the Pink Panther, The (1974)
+164 Abyss, The (1989)
+165 Jean de Florette (1986)
+166 Manon of the Spring (Manon des sources) (1986)
+167 Private Benjamin (1980)
+168 Monty Python and the Holy Grail (1974)
+169 Wrong Trousers, The (1993)
+170 Cinema Paradiso (1988)
+171 Delicatessen (1991)
+172 Empire Strikes Back, The (1980)
+173 Princess Bride, The (1987)
+174 Raiders of the Lost Ark (1981)
+175 Brazil (1985)
+176 Aliens (1986)
+177 Good, The Bad and The Ugly, The (1966)
+178 12 Angry Men (1957)
+179 Clockwork Orange, A (1971)
+180 Apocalypse Now (1979)
+181 Return of the Jedi (1983)
+182 GoodFellas (1990)
+183 Alien (1979)
+184 Army of Darkness (1993)
+185 Psycho (1960)
+186 Blues Brothers, The (1980)
+187 Godfather: Part II, The (1974)
+188 Full Metal Jacket (1987)
+189 Grand Day Out, A (1992)
+190 Henry V (1989)
+191 Amadeus (1984)
+192 Raging Bull (1980)
+193 Right Stuff, The (1983)
+194 Sting, The (1973)
+195 Terminator, The (1984)
+196 Dead Poets Society (1989)
+197 Graduate, The (1967)
+198 Nikita (La Femme Nikita) (1990)
+199 Bridge on the River Kwai, The (1957)
+200 Shining, The (1980)
+201 Evil Dead II (1987)
+202 Groundhog Day (1993)
+203 Unforgiven (1992)
+204 Back to the Future (1985)
+205 Patton (1970)
+206 Akira (1988)
+207 Cyrano de Bergerac (1990)
+208 Young Frankenstein (1974)
+209 This Is Spinal Tap (1984)
+210 Indiana Jones and the Last Crusade (1989)
+211 M*A*S*H (1970)
+212 Unbearable Lightness of Being, The (1988)
+213 Room with a View, A (1986)
+214 Pink Floyd - The Wall (1982)
+215 Field of Dreams (1989)
+216 When Harry Met Sally... (1989)
+217 Bram Stoker's Dracula (1992)
+218 Cape Fear (1991)
+219 Nightmare on Elm Street, A (1984)
+220 Mirror Has Two Faces, The (1996)
+221 Breaking the Waves (1996)
+222 Star Trek: First Contact (1996)
+223 Sling Blade (1996)
+224 Ridicule (1996)
+225 101 Dalmatians (1996)
+226 Die Hard 2 (1990)
+227 Star Trek VI: The Undiscovered Country (1991)
+228 Star Trek: The Wrath of Khan (1982)
+229 Star Trek III: The Search for Spock (1984)
+230 Star Trek IV: The Voyage Home (1986)
+231 Batman Returns (1992)
+232 Young Guns (1988)
+233 Under Siege (1992)
+234 Jaws (1975)
+235 Mars Attacks! (1996)
+236 Citizen Ruth (1996)
+237 Jerry Maguire (1996)
+238 Raising Arizona (1987)
+239 Sneakers (1992)
+240 Beavis and Butt-head Do America (1996)
+241 Last of the Mohicans, The (1992)
+242 Kolya (1996)
+243 Jungle2Jungle (1997)
+244 Smilla's Sense of Snow (1997)
+245 Devil's Own, The (1997)
+246 Chasing Amy (1997)
+247 Turbo: A Power Rangers Movie (1997)
+248 Grosse Pointe Blank (1997)
+249 Austin Powers: International Man of Mystery (1997)
+250 Fifth Element, The (1997)
+251 Shall We Dance? (1996)
+252 Lost World: Jurassic Park, The (1997)
+253 Pillow Book, The (1995)
+254 Batman & Robin (1997)
+255 My Best Friend's Wedding (1997)
+256 When the Cats Away (Chacun cherche son chat) (1996)
+257 Men in Black (1997)
+258 Contact (1997)
+259 George of the Jungle (1997)
+260 Event Horizon (1997)
+261 Air Bud (1997)
+262 In the Company of Men (1997)
+263 Steel (1997)
+264 Mimic (1997)
+265 Hunt for Red October, The (1990)
+266 Kull the Conqueror (1997)
+267 unknown
+268 Chasing Amy (1997)
+269 Full Monty, The (1997)
+270 Gattaca (1997)
+271 Starship Troopers (1997)
+272 Good Will Hunting (1997)
+273 Heat (1995)
+274 Sabrina (1995)
+275 Sense and Sensibility (1995)
+276 Leaving Las Vegas (1995)
+277 Restoration (1995)
+278 Bed of Roses (1996)
+279 Once Upon a Time... When We Were Colored (1995)
+280 Up Close and Personal (1996)
+281 River Wild, The (1994)
+282 Time to Kill, A (1996)
+283 Emma (1996)
+284 Tin Cup (1996)
+285 Secrets & Lies (1996)
+286 English Patient, The (1996)
+287 Marvin's Room (1996)
+288 Scream (1996)
+289 Evita (1996)
+290 Fierce Creatures (1997)
+291 Absolute Power (1997)
+292 Rosewood (1997)
+293 Donnie Brasco (1997)
+294 Liar Liar (1997)
+295 Breakdown (1997)
+296 Promesse, La (1996)
+297 Ulee's Gold (1997)
+298 Face/Off (1997)
+299 Hoodlum (1997)
+300 Air Force One (1997)
+301 In & Out (1997)
+302 L.A. Confidential (1997)
+303 Ulee's Gold (1997)
+304 Fly Away Home (1996)
+305 Ice Storm, The (1997)
+306 Mrs. Brown (Her Majesty, Mrs. Brown) (1997)
+307 Devil's Advocate, The (1997)
+308 FairyTale: A True Story (1997)
+309 Deceiver (1997)
+310 Rainmaker, The (1997)
+311 Wings of the Dove, The (1997)
+312 Midnight in the Garden of Good and Evil (1997)
+313 Titanic (1997)
+314 3 Ninjas: High Noon At Mega Mountain (1998)
+315 Apt Pupil (1998)
+316 As Good As It Gets (1997)
+317 In the Name of the Father (1993)
+318 Schindler's List (1993)
+319 Everyone Says I Love You (1996)
+320 Paradise Lost: The Child Murders at Robin Hood Hills (1996)
+321 Mother (1996)
+322 Murder at 1600 (1997)
+323 Dante's Peak (1997)
+324 Lost Highway (1997)
+325 Crash (1996)
+326 G.I. Jane (1997)
+327 Cop Land (1997)
+328 Conspiracy Theory (1997)
+329 Desperate Measures (1998)
+330 187 (1997)
+331 Edge, The (1997)
+332 Kiss the Girls (1997)
+333 Game, The (1997)
+334 U Turn (1997)
+335 How to Be a Player (1997)
+336 Playing God (1997)
+337 House of Yes, The (1997)
+338 Bean (1997)
+339 Mad City (1997)
+340 Boogie Nights (1997)
+341 Critical Care (1997)
+342 Man Who Knew Too Little, The (1997)
+343 Alien: Resurrection (1997)
+344 Apostle, The (1997)
+345 Deconstructing Harry (1997)
+346 Jackie Brown (1997)
+347 Wag the Dog (1997)
+348 Desperate Measures (1998)
+349 Hard Rain (1998)
+350 Fallen (1998)
+351 Prophecy II, The (1998)
+352 Spice World (1997)
+353 Deep Rising (1998)
+354 Wedding Singer, The (1998)
+355 Sphere (1998)
+356 Client, The (1994)
+357 One Flew Over the Cuckoo's Nest (1975)
+358 Spawn (1997)
+359 Assignment, The (1997)
+360 Wonderland (1997)
+361 Incognito (1997)
+362 Blues Brothers 2000 (1998)
+363 Sudden Death (1995)
+364 Ace Ventura: When Nature Calls (1995)
+365 Powder (1995)
+366 Dangerous Minds (1995)
+367 Clueless (1995)
+368 Bio-Dome (1996)
+369 Black Sheep (1996)
+370 Mary Reilly (1996)
+371 Bridges of Madison County, The (1995)
+372 Jeffrey (1995)
+373 Judge Dredd (1995)
+374 Mighty Morphin Power Rangers: The Movie (1995)
+375 Showgirls (1995)
+376 Houseguest (1994)
+377 Heavyweights (1994)
+378 Miracle on 34th Street (1994)
+379 Tales From the Crypt Presents: Demon Knight (1995)
+380 Star Trek: Generations (1994)
+381 Muriel's Wedding (1994)
+382 Adventures of Priscilla, Queen of the Desert, The (1994)
+383 Flintstones, The (1994)
+384 Naked Gun 33 1/3: The Final Insult (1994)
+385 True Lies (1994)
+386 Addams Family Values (1993)
+387 Age of Innocence, The (1993)
+388 Beverly Hills Cop III (1994)
+389 Black Beauty (1994)
+390 Fear of a Black Hat (1993)
+391 Last Action Hero (1993)
+392 Man Without a Face, The (1993)
+393 Mrs. Doubtfire (1993)
+394 Radioland Murders (1994)
+395 Robin Hood: Men in Tights (1993)
+396 Serial Mom (1994)
+397 Striking Distance (1993)
+398 Super Mario Bros. (1993)
+399 Three Musketeers, The (1993)
+400 Little Rascals, The (1994)
+401 Brady Bunch Movie, The (1995)
+402 Ghost (1990)
+403 Batman (1989)
+404 Pinocchio (1940)
+405 Mission: Impossible (1996)
+406 Thinner (1996)
+407 Spy Hard (1996)
+408 Close Shave, A (1995)
+409 Jack (1996)
+410 Kingpin (1996)
+411 Nutty Professor, The (1996)
+412 Very Brady Sequel, A (1996)
+413 Tales from the Crypt Presents: Bordello of Blood (1996)
+414 My Favorite Year (1982)
+415 Apple Dumpling Gang, The (1975)
+416 Old Yeller (1957)
+417 Parent Trap, The (1961)
+418 Cinderella (1950)
+419 Mary Poppins (1964)
+420 Alice in Wonderland (1951)
+421 William Shakespeare's Romeo and Juliet (1996)
+422 Aladdin and the King of Thieves (1996)
+423 E.T. the Extra-Terrestrial (1982)
+424 Children of the Corn: The Gathering (1996)
+425 Bob Roberts (1992)
+426 Transformers: The Movie, The (1986)
+427 To Kill a Mockingbird (1962)
+428 Harold and Maude (1971)
+429 Day the Earth Stood Still, The (1951)
+430 Duck Soup (1933)
+431 Highlander (1986)
+432 Fantasia (1940)
+433 Heathers (1989)
+434 Forbidden Planet (1956)
+435 Butch Cassidy and the Sundance Kid (1969)
+436 American Werewolf in London, An (1981)
+437 Amityville 1992: It's About Time (1992)
+438 Amityville 3-D (1983)
+439 Amityville: A New Generation (1993)
+440 Amityville II: The Possession (1982)
+441 Amityville Horror, The (1979)
+442 Amityville Curse, The (1990)
+443 Birds, The (1963)
+444 Blob, The (1958)
+445 Body Snatcher, The (1945)
+446 Burnt Offerings (1976)
+447 Carrie (1976)
+448 Omen, The (1976)
+449 Star Trek: The Motion Picture (1979)
+450 Star Trek V: The Final Frontier (1989)
+451 Grease (1978)
+452 Jaws 2 (1978)
+453 Jaws 3-D (1983)
+454 Bastard Out of Carolina (1996)
+455 Jackie Chan's First Strike (1996)
+456 Beverly Hills Ninja (1997)
+457 Free Willy 3: The Rescue (1997)
+458 Nixon (1995)
+459 Cry, the Beloved Country (1995)
+460 Crossing Guard, The (1995)
+461 Smoke (1995)
+462 Like Water For Chocolate (Como agua para chocolate) (1992)
+463 Secret of Roan Inish, The (1994)
+464 Vanya on 42nd Street (1994)
+465 Jungle Book, The (1994)
+466 Red Rock West (1992)
+467 Bronx Tale, A (1993)
+468 Rudy (1993)
+469 Short Cuts (1993)
+470 Tombstone (1993)
+471 Courage Under Fire (1996)
+472 Dragonheart (1996)
+473 James and the Giant Peach (1996)
+474 Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)
+475 Trainspotting (1996)
+476 First Wives Club, The (1996)
+477 Matilda (1996)
+478 Philadelphia Story, The (1940)
+479 Vertigo (1958)
+480 North by Northwest (1959)
+481 Apartment, The (1960)
+482 Some Like It Hot (1959)
+483 Casablanca (1942)
+484 Maltese Falcon, The (1941)
+485 My Fair Lady (1964)
+486 Sabrina (1954)
+487 Roman Holiday (1953)
+488 Sunset Blvd. (1950)
+489 Notorious (1946)
+490 To Catch a Thief (1955)
+491 Adventures of Robin Hood, The (1938)
+492 East of Eden (1955)
+493 Thin Man, The (1934)
+494 His Girl Friday (1940)
+495 Around the World in 80 Days (1956)
+496 It's a Wonderful Life (1946)
+497 Bringing Up Baby (1938)
+498 African Queen, The (1951)
+499 Cat on a Hot Tin Roof (1958)
+500 Fly Away Home (1996)
+501 Dumbo (1941)
+502 Bananas (1971)
+503 Candidate, The (1972)
+504 Bonnie and Clyde (1967)
+505 Dial M for Murder (1954)
+506 Rebel Without a Cause (1955)
+507 Streetcar Named Desire, A (1951)
+508 People vs. Larry Flynt, The (1996)
+509 My Left Foot (1989)
+510 Magnificent Seven, The (1954)
+511 Lawrence of Arabia (1962)
+512 Wings of Desire (1987)
+513 Third Man, The (1949)
+514 Annie Hall (1977)
+515 Boot, Das (1981)
+516 Local Hero (1983)
+517 Manhattan (1979)
+518 Miller's Crossing (1990)
+519 Treasure of the Sierra Madre, The (1948)
+520 Great Escape, The (1963)
+521 Deer Hunter, The (1978)
+522 Down by Law (1986)
+523 Cool Hand Luke (1967)
+524 Great Dictator, The (1940)
+525 Big Sleep, The (1946)
+526 Ben-Hur (1959)
+527 Gandhi (1982)
+528 Killing Fields, The (1984)
+529 My Life as a Dog (Mitt liv som hund) (1985)
+530 Man Who Would Be King, The (1975)
+531 Shine (1996)
+532 Kama Sutra: A Tale of Love (1996)
+533 Daytrippers, The (1996)
+534 Traveller (1997)
+535 Addicted to Love (1997)
+536 Ponette (1996)
+537 My Own Private Idaho (1991)
+538 Anastasia (1997)
+539 Mouse Hunt (1997)
+540 Money Train (1995)
+541 Mortal Kombat (1995)
+542 Pocahontas (1995)
+543 Mis�rables, Les (1995)
+544 Things to Do in Denver when You're Dead (1995)
+545 Vampire in Brooklyn (1995)
+546 Broken Arrow (1996)
+547 Young Poisoner's Handbook, The (1995)
+548 NeverEnding Story III, The (1994)
+549 Rob Roy (1995)
+550 Die Hard: With a Vengeance (1995)
+551 Lord of Illusions (1995)
+552 Species (1995)
+553 Walk in the Clouds, A (1995)
+554 Waterworld (1995)
+555 White Man's Burden (1995)
+556 Wild Bill (1995)
+557 Farinelli: il castrato (1994)
+558 Heavenly Creatures (1994)
+559 Interview with the Vampire (1994)
+560 Kid in King Arthur's Court, A (1995)
+561 Mary Shelley's Frankenstein (1994)
+562 Quick and the Dead, The (1995)
+563 Stephen King's The Langoliers (1995)
+564 Tales from the Hood (1995)
+565 Village of the Damned (1995)
+566 Clear and Present Danger (1994)
+567 Wes Craven's New Nightmare (1994)
+568 Speed (1994)
+569 Wolf (1994)
+570 Wyatt Earp (1994)
+571 Another Stakeout (1993)
+572 Blown Away (1994)
+573 Body Snatchers (1993)
+574 Boxing Helena (1993)
+575 City Slickers II: The Legend of Curly's Gold (1994)
+576 Cliffhanger (1993)
+577 Coneheads (1993)
+578 Demolition Man (1993)
+579 Fatal Instinct (1993)
+580 Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995)
+581 Kalifornia (1993)
+582 Piano, The (1993)
+583 Romeo Is Bleeding (1993)
+584 Secret Garden, The (1993)
+585 Son in Law (1993)
+586 Terminal Velocity (1994)
+587 Hour of the Pig, The (1993)
+588 Beauty and the Beast (1991)
+589 Wild Bunch, The (1969)
+590 Hellraiser: Bloodline (1996)
+591 Primal Fear (1996)
+592 True Crime (1995)
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+1273 Color of Night (1994)
+1274 Robocop 3 (1993)
+1275 Killer (Bulletproof Heart) (1994)
+1276 Sunset Park (1996)
+1277 Set It Off (1996)
+1278 Selena (1997)
+1279 Wild America (1997)
+1280 Gang Related (1997)
+1281 Manny & Lo (1996)
+1282 Grass Harp, The (1995)
+1283 Out to Sea (1997)
+1284 Before and After (1996)
+1285 Princess Caraboo (1994)
+1286 Shall We Dance? (1937)
+1287 Ed (1996)
+1288 Denise Calls Up (1995)
+1289 Jack and Sarah (1995)
+1290 Country Life (1994)
+1291 Celtic Pride (1996)
+1292 Simple Wish, A (1997)
+1293 Star Kid (1997)
+1294 Ayn Rand: A Sense of Life (1997)
+1295 Kicked in the Head (1997)
+1296 Indian Summer (1996)
+1297 Love Affair (1994)
+1298 Band Wagon, The (1953)
+1299 Penny Serenade (1941)
+1300 'Til There Was You (1997)
+1301 Stripes (1981)
+1302 Late Bloomers (1996)
+1303 Getaway, The (1994)
+1304 New York Cop (1996)
+1305 National Lampoon's Senior Trip (1995)
+1306 Delta of Venus (1994)
+1307 Carmen Miranda: Bananas Is My Business (1994)
+1308 Babyfever (1994)
+1309 Very Natural Thing, A (1974)
+1310 Walk in the Sun, A (1945)
+1311 Waiting to Exhale (1995)
+1312 Pompatus of Love, The (1996)
+1313 Palmetto (1998)
+1314 Surviving the Game (1994)
+1315 Inventing the Abbotts (1997)
+1316 Horse Whisperer, The (1998)
+1317 Journey of August King, The (1995)
+1318 Catwalk (1995)
+1319 Neon Bible, The (1995)
+1320 Homage (1995)
+1321 Open Season (1996)
+1322 Metisse (Caf� au Lait) (1993)
+1323 Wooden Man's Bride, The (Wu Kui) (1994)
+1324 Loaded (1994)
+1325 August (1996)
+1326 Boys (1996)
+1327 Captives (1994)
+1328 Of Love and Shadows (1994)
+1329 Low Life, The (1994)
+1330 An Unforgettable Summer (1994)
+1331 Last Klezmer: Leopold Kozlowski, His Life and Music, The (1995)
+1332 My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993)
+1333 Midnight Dancers (Sibak) (1994)
+1334 Somebody to Love (1994)
+1335 American Buffalo (1996)
+1336 Kazaam (1996)
+1337 Larger Than Life (1996)