getauto.m

如果你看过神经模糊与软运算这本书,相信你一定想得到它的源代码.

% ====== Get data from original data file
data_file = 'auto-mpg.dat';
fprintf('Getting data from file ''%s''...\n', data_file);
pause(0.1);
fid = fopen(data_file);

raw_data = zeros(500, 8);
i = 1;
while 1
	line = fgetl(fid);
	if ~isstr(line), break, end
	if isempty(find(line=='?'))	% get rid of data with missing value
		raw_data(i, :) = sscanf(line, '%f')';
		i = i+1;
	end
end
fclose(fid);
raw_data(i:500, :) = [];

% Rearrange the data so that inputs come first
data = [raw_data(:, 2:7) raw_data(:, 1)];

% ====== split into training and checking data
fprintf('Doing data preprocessing...\n'); 
% == Data normalization: everything is within [0 1]
%col_min = min(data);
%col_range = range(data);
%data_n = size(data, 1);
%tmp = ones(data_n, 1);
%normalized_data = (data-col_min(tmp, :))./col_range(tmp, :);

% Get training and checking data
%trn_data = normalized_data(1:2:size(data, 1), :);
%chk_data = normalized_data(2:2:size(data, 1), :);
trn_data = data(1:2:size(data, 1), :);
chk_data = data(2:2:size(data, 1), :);

% ====== Get variable names
%   1. mpg:           continuous
%   2. cylinders:     multi-valued discrete
%   3. displacement:  continuous
%   4. horsepower:    continuous
%   5. weight:        continuous
%   6. acceleration:  continuous
%   7. model year:    multi-valued discrete
%   8. origin:        multi-valued discrete
%   9. car name:      string (unique for each instance)

var_name = str2mat('cylinder', 'disp', 'power', 'weight', 'acceler', 'year', 'MPG');

% ====== Do linear regression
fprintf('Doing linear regression...\n'); 
fprintf('Linear regression with %d linear parameters:\n', size(data, 2)); 
A_trn = [trn_data(:, 1:size(data,2)-1) ones(size(trn_data,1), 1)]; 
B_trn = trn_data(:, size(data,2));
coef = A_trn\B_trn;
trn_error = norm(A_trn*coef-B_trn)/sqrt(size(trn_data,1));

A_chk = [chk_data(:, 1:size(data,2)-1) ones(size(chk_data,1), 1)]; 
B_chk = chk_data(:, size(data,2));
chk_error = norm(A_chk*coef-B_chk)/sqrt(size(chk_data,1));

fprintf('\tRMSE for training data: %g\n', trn_error); 
fprintf('\tRMSE for checking data: %g\n', chk_error);