谁能告诉我matlab程序c4.5算法怎么调用吗？下面是程序代码，告诉我怎么调用（具体的使用举例）

matlab程序c4.5算法怎么调用吗~

function test_targets = C4_5(train_patterns, train_targets, test_patterns, inc_node, Nu)

% Classify using Quinlan's C4.5 algorithm
% Inputs:
% training_patterns - Train patterns 模式
%training_targets- Train targets 目标
% test_patterns - Test patterns 测试模式
%inc_node - Percentage of incorrectly assigned samples at a node
% 一个节点中不正确的样本分配比例
% Outputs
%test_targets - Predicted targets 预测目标

%NOTE: In this implementation it is assumed that a pattern vector with fewer than 10 unique values (the parameter Nu)
%is discrete, and will be treated as such. Other vectors will be treated as continuous

[Ni, M]= size(train_patterns);
inc_node = inc_node*M/100;

%Find which of the input patterns are discrete, and discretisize the corresponding
%dimension on the test patterns
discrete_dim = zeros(1,Ni);
for i = 1:Ni,
Ub = unique(train_patterns(i,:));%获得第i行中不重复的元素构成的向量
Nb = length(Ub);
if (Nb <= Nu),
%This is a discrete pattern 这是一个离散的模式
discrete_dim(i)= Nb;
dist = abs(ones(Nb ,1)*test_patterns(i,:) - Ub'*ones(1, size(test_patterns,2)));
[m, in] = min(dist);
test_patterns(i,:) = Ub(in);
end
end

%Build the tree recursively
%disp('Building tree')
tree = make_tree(train_patterns, train_targets, inc_node, discrete_dim, max(discrete_dim), 0);

%Classify test samples
%disp('Classify test samples using the tree')
test_targets = use_tree(test_patterns, 1:size(test_patterns,2), tree, discrete_dim, unique(train_targets));

%END

function targets = use_tree(patterns, indices, tree, discrete_dim, Uc)
%Classify recursively using a tree

targets = zeros(1, size(patterns,2));

if (tree.dim == 0)
%Reached the end of the tree
targets(indices) = tree.child;
return
end

%This is not the last level of the tree, so:
%First, find the dimension we are to work on
dim = tree.dim;
dims= 1:size(patterns,1);

%And classify according to it
if (discrete_dim(dim) == 0),
%Continuous pattern
in= indices(find(patterns(dim, indices) <= tree.split_loc));
targets= targets + use_tree(patterns(dims, :), in, tree.child(1), discrete_dim(dims), Uc);
in= indices(find(patterns(dim, indices) > tree.split_loc));
targets= targets + use_tree(patterns(dims, :), in, tree.child(2), discrete_dim(dims), Uc);
else
%Discrete pattern
Uf= unique(patterns(dim,:));
for i = 1:length(Uf),
if any(Uf(i) == tree.Nf) %Has this sort of data appeared before? If not, do nothing
in = indices(find(patterns(dim, indices) == Uf(i)));
targets= targets + use_tree(patterns(dims, :), in, tree.child(find(Uf(i)==tree.Nf)), discrete_dim(dims), Uc);
end
end
end

%END use_tree

function tree = make_tree(patterns, targets, inc_node, discrete_dim, maxNbin, base)
%Build a tree recursively

[Ni, L] = size(patterns);
Uc = unique(targets);
tree.dim= 0;
%tree.child(1:maxNbin)= zeros(1,maxNbin);
tree.split_loc= inf;

if isempty(patterns),
return
end

%When to stop: If the dimension is one or the number of examples is small
if ((inc_node > L) | (L == 1) | (length(Uc) == 1)),
H= hist(targets, length(Uc));
[m, largest] = max(H);
tree.Nf = [];
tree.split_loc = [];
tree.child = Uc(largest);
return
end

%Compute the node's I
for i = 1:length(Uc),
Pnode(i) = length(find(targets == Uc(i))) / L;
end
Inode = -sum(Pnode.*log(Pnode)/log(2));

%For each dimension, compute the gain ratio impurity
%This is done separately for discrete and continuous patterns
delta_Ib = zeros(1, Ni);
split_loc= ones(1, Ni)*inf;

for i = 1:Ni,
data= patterns(i,:);
Ud = unique(data);
Nbins= length(Ud);
if (discrete_dim(i)),
%This is a discrete pattern
P= zeros(length(Uc), Nbins);
for j = 1:length(Uc),
for k = 1:Nbins,
indices = find((targets == Uc(j)) & (patterns(i,:) == Ud(k)));
P(j,k) = length(indices);
end
end
Pk = sum(P);
P = P/L;
Pk = Pk/sum(Pk);
info = sum(-P.*log(eps+P)/log(2));
delta_Ib(i) = (Inode-sum(Pk.*info))/-sum(Pk.*log(eps+Pk)/log(2));
else
%This is a continuous pattern
P= zeros(length(Uc), 2);

%Sort the patterns
[sorted_data, indices] = sort(data);
sorted_targets = targets(indices);

%Calculate the information for each possible split
I= zeros(1, L-1);
for j = 1:L-1,
%for k =1:length(Uc),
% P(k,1) = sum(sorted_targets(1:j) == Uc(k));
% P(k,2) = sum(sorted_targets(j+1:end) == Uc(k));
%end
P(:, 1) = hist(sorted_targets(1:j) , Uc);
P(:, 2) = hist(sorted_targets(j+1:end) , Uc);
Ps= sum(P)/L;
P= P/L;

Pk = sum(P);
P1 = repmat(Pk, length(Uc), 1);
P1 = P1 + eps*(P1==0);

info= sum(-P.*log(eps+P./P1)/log(2));
I(j)= Inode - sum(info.*Ps);
end
[delta_Ib(i), s] = max(I);
split_loc(i) = sorted_data(s);
end
end

%Find the dimension minimizing delta_Ib
[m, dim] = max(delta_Ib);
dims = 1:Ni;
tree.dim = dim;

%Split along the 'dim' dimension
Nf= unique(patterns(dim,:));
Nbins= length(Nf);
tree.Nf = Nf;
tree.split_loc = split_loc(dim);

%If only one value remains for this pattern, one cannot split it.
if (Nbins == 1)
H= hist(targets, length(Uc));
[m, largest] = max(H);
tree.Nf = [];
tree.split_loc = [];
tree.child = Uc(largest);
return
end

if (discrete_dim(dim)),
%Discrete pattern
for i = 1:Nbins,
indices = find(patterns(dim, :) == Nf(i));
tree.child(i)= make_tree(patterns(dims, indices), targets(indices), inc_node, discrete_dim(dims), maxNbin, base);
end
else
%Continuous pattern
indices1 = find(patterns(dim,:) <= split_loc(dim));
indices2 = find(patterns(dim,:) > split_loc(dim));
if ~(isempty(indices1) | isempty(indices2))
tree.child(1)= make_tree(patterns(dims, indices1), targets(indices1), inc_node, discrete_dim(dims), maxNbin, base+1);
tree.child(2)= make_tree(patterns(dims, indices2), targets(indices2), inc_node, discrete_dim(dims), maxNbin, base+1);
else
H= hist(targets, length(Uc));
[m, largest] = max(H);
tree.child = Uc(largest);
tree.dim = 0;
end
end

将代码保存在matlab中就可以用了
例如：function[poly]=polyadd(poly1,poly2)
%polyadd(poly1,poly2) adds two polynominals possibly of uneven length
if length(poly1)<length(poly2)
short=poly1;
long=poly2;
else
short=poly2;
long=poly1;
end
mz=length(long)-length(short);
if mz>0
poly=[zeros(1,mz),short]+long;
else
poly=long+short;
end
保存名为polyadd.M 在matlab中输入polyadd就可以调用了

function test_targets = C4_5(train_patterns, train_targets, test_patterns, inc_node, Nu)

% Classify using Quinlan's C4.5 algorithm
% Inputs:
% training_patterns - Train patterns 模式
% training_targets - Train targets 目标
% test_patterns - Test patterns 测试模式
% inc_node - Percentage of incorrectly assigned samples at a node
% 一个节点中不正确的样本分配比例
% Outputs
% test_targets - Predicted targets 预测目标

%NOTE: In this implementation it is assumed that a pattern vector with fewer than 10 unique values (the parameter Nu)
%is discrete, and will be treated as such. Other vectors will be treated as continuous

[Ni, M] = size(train_patterns);
inc_node = inc_node*M/100;

%Find which of the input patterns are discrete, and discretisize the corresponding
%dimension on the test patterns
discrete_dim = zeros(1,Ni);
for i = 1:Ni,
Ub = unique(train_patterns(i,:));%获得第i行中不重复的元素构成的向量
Nb = length(Ub);
if (Nb <= Nu),
%This is a discrete pattern 这是一个离散的模式
discrete_dim(i) = Nb;
dist = abs(ones(Nb ,1)*test_patterns(i,:) - Ub'*ones(1, size(test_patterns,2)));
[m, in] = min(dist);
test_patterns(i,:) = Ub(in);
end
end

%Build the tree recursively
%disp('Building tree')
tree = make_tree(train_patterns, train_targets, inc_node, discrete_dim, max(discrete_dim), 0);

%Classify test samples
%disp('Classify test samples using the tree')
test_targets = use_tree(test_patterns, 1:size(test_patterns,2), tree, discrete_dim, unique(train_targets));

%END

function targets = use_tree(patterns, indices, tree, discrete_dim, Uc)
%Classify recursively using a tree

targets = zeros(1, size(patterns,2));

if (tree.dim == 0)
%Reached the end of the tree
targets(indices) = tree.child;
return
end

%This is not the last level of the tree, so:
%First, find the dimension we are to work on
dim = tree.dim;
dims= 1:size(patterns,1);

%And classify according to it
if (discrete_dim(dim) == 0),
%Continuous pattern
in = indices(find(patterns(dim, indices) <= tree.split_loc));
targets = targets + use_tree(patterns(dims, :), in, tree.child(1), discrete_dim(dims), Uc);
in = indices(find(patterns(dim, indices) > tree.split_loc));
targets = targets + use_tree(patterns(dims, :), in, tree.child(2), discrete_dim(dims), Uc);
else
%Discrete pattern
Uf = unique(patterns(dim,:));
for i = 1:length(Uf),
if any(Uf(i) == tree.Nf) %Has this sort of data appeared before? If not, do nothing
in = indices(find(patterns(dim, indices) == Uf(i)));
targets = targets + use_tree(patterns(dims, :), in, tree.child(find(Uf(i)==tree.Nf)), discrete_dim(dims), Uc);
end
end
end

%END use_tree

function tree = make_tree(patterns, targets, inc_node, discrete_dim, maxNbin, base)
%Build a tree recursively

[Ni, L] = size(patterns);
Uc = unique(targets);
tree.dim = 0;
%tree.child(1:maxNbin) = zeros(1,maxNbin);
tree.split_loc = inf;

if isempty(patterns),
return
end

%When to stop: If the dimension is one or the number of examples is small
if ((inc_node > L) | (L == 1) | (length(Uc) == 1)),
H = hist(targets, length(Uc));
[m, largest] = max(H);
tree.Nf = [];
tree.split_loc = [];
tree.child = Uc(largest);
return
end

%Compute the node's I
for i = 1:length(Uc),
Pnode(i) = length(find(targets == Uc(i))) / L;
end
Inode = -sum(Pnode.*log(Pnode)/log(2));

%For each dimension, compute the gain ratio impurity
%This is done separately for discrete and continuous patterns
delta_Ib = zeros(1, Ni);
split_loc = ones(1, Ni)*inf;

for i = 1:Ni,
data = patterns(i,:);
Ud = unique(data);
Nbins = length(Ud);
if (discrete_dim(i)),
%This is a discrete pattern
P = zeros(length(Uc), Nbins);
for j = 1:length(Uc),
for k = 1:Nbins,
indices = find((targets == Uc(j)) & (patterns(i,:) == Ud(k)));
P(j,k) = length(indices);
end
end
Pk = sum(P);
P = P/L;
Pk = Pk/sum(Pk);
info = sum(-P.*log(eps+P)/log(2));
delta_Ib(i) = (Inode-sum(Pk.*info))/-sum(Pk.*log(eps+Pk)/log(2));
else
%This is a continuous pattern
P = zeros(length(Uc), 2);

%Sort the patterns
[sorted_data, indices] = sort(data);
sorted_targets = targets(indices);

%Calculate the information for each possible split
I = zeros(1, L-1);
for j = 1:L-1,
%for k =1:length(Uc),
% P(k,1) = sum(sorted_targets(1:j) == Uc(k));
% P(k,2) = sum(sorted_targets(j+1:end) == Uc(k));
%end
P(:, 1) = hist(sorted_targets(1:j) , Uc);
P(:, 2) = hist(sorted_targets(j+1:end) , Uc);
Ps = sum(P)/L;
P = P/L;

Pk = sum(P);
P1 = repmat(Pk, length(Uc), 1);
P1 = P1 + eps*(P1==0);

info = sum(-P.*log(eps+P./P1)/log(2));
I(j) = Inode - sum(info.*Ps);
end
[delta_Ib(i), s] = max(I);
split_loc(i) = sorted_data(s);
end
end

%Find the dimension minimizing delta_Ib
[m, dim] = max(delta_Ib);
dims = 1:Ni;
tree.dim = dim;

%Split along the 'dim' dimension
Nf = unique(patterns(dim,:));
Nbins = length(Nf);
tree.Nf = Nf;
tree.split_loc = split_loc(dim);

%If only one value remains for this pattern, one cannot split it.
if (Nbins == 1)
H = hist(targets, length(Uc));
[m, largest] = max(H);
tree.Nf = [];
tree.split_loc = [];
tree.child = Uc(largest);
return
end

if (discrete_dim(dim)),
%Discrete pattern
for i = 1:Nbins,
indices = find(patterns(dim, :) == Nf(i));
tree.child(i) = make_tree(patterns(dims, indices), targets(indices), inc_node, discrete_dim(dims), maxNbin, base);
end
else
%Continuous pattern
indices1 = find(patterns(dim,:) <= split_loc(dim));
indices2 = find(patterns(dim,:) > split_loc(dim));
if ~(isempty(indices1) | isempty(indices2))
tree.child(1) = make_tree(patterns(dims, indices1), targets(indices1), inc_node, discrete_dim(dims), maxNbin, base+1);
tree.child(2) = make_tree(patterns(dims, indices2), targets(indices2), inc_node, discrete_dim(dims), maxNbin, base+1);
else
H = hist(targets, length(Uc));
[m, largest] = max(H);
tree.child = Uc(largest);
tree.dim = 0;
end
end

这是该题的程序，请给出调用举例~~~急~

对train_patterns, train_targets, test_patterns, inc_node, Nu这几个参数赋相应的值，在matlab命令窗口中直接输入c4.5回车就行了

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北湖区13448614556： 谁能告诉我matlab程序c4.5算法怎么调用吗?下面是程序代码,告诉我怎么调用(具体的使用举例) - ？
晋苛韦迪： function test_targets = C4_5(train_patterns, train_targets, test_patterns, inc_node, Nu)% Classify using Quinlan's C4.5 algorithm% Inputs:% training_patterns - Train patterns 模式% training_targets - Train targets 目标% test_patterns - Test ...

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