Adjust code to perform to C++ standard according to gcc-3.4
interpretation... (Have not compiled/checked/changed paradisEO.) That is, the current code compiles with gcc-3.4 and the checks (besides t-MGE1bit) all pass.
This commit is contained in:
kuepper
parent
faaadf7599
commit
85a326c5e4
35 changed files with 1057 additions and 864 deletions
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@ -48,7 +48,7 @@ namespace std {
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copy(v.begin(), v.end(), oi);
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return os;
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}
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istream& operator>>(istream& is, mlp::vector& v)
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{
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for (mlp::vector::iterator vi = v.begin() ; vi != v.end() ; vi++) {
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@ -63,8 +63,8 @@ namespace mlp
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//---------------------------------------------------------------------------
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// useful typedefs
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//---------------------------------------------------------------------------
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const real max_real = MLP_MAXFLOAT;
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const real min_real = MLP_MINFLOAT;
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@ -72,38 +72,38 @@ namespace mlp
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//---------------------------------------------------------------------------
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// sigmoid
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//---------------------------------------------------------------------------
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real sigmoid(const real& x)
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{
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return 1.0 / (1.0 + exp(-x));
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}
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//---------------------------------------------------------------------------
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// neuron
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//---------------------------------------------------------------------------
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struct neuron
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{
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real bias;
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vector weight;
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neuron(const unsigned& num_inputs = 0): weight(num_inputs) {}
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void reset()
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{
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normal_generator<real> rnd(1.0);
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bias = rnd();
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generate(weight.begin(), weight.end(), rnd);
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}
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real operator()(const vector& input) const
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{
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return sigmoid(bias + weight * input);
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}
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unsigned length() const { return weight.size() + 1; }
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void normalize()
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{
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real n = sqrt(bias * bias + weight * weight);
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@ -114,7 +114,7 @@ namespace mlp
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void desaturate()
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{
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bias = -5.0 + 10.0 / (1.0 + exp(bias / -5.0));
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for (vector::iterator w = weight.begin(); w != weight.end(); ++w)
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*w = -5.0 + 10.0 / (1.0 + exp(*w / -5.0));
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}
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@ -127,7 +127,7 @@ namespace mlp
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void perturb(double magnitude = 0.3, double probability = 1.0)
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{
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for (vector::iterator w = weight.begin(); w != weight.end(); ++w)
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if ( probability >= 1.0 || drand48() < probability)
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perturb_num(*w, magnitude);
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@ -143,7 +143,7 @@ namespace std {
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{
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return os << n.bias << " " << n.weight;
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}
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istream& operator>>(istream& is, mlp::neuron& n)
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{
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return is >> n.bias >> n.weight;
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@ -152,15 +152,15 @@ namespace std {
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}
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namespace mlp {
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//---------------------------------------------------------------------------
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// layer
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//---------------------------------------------------------------------------
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class layer: public std::vector<neuron>
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{
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public:
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layer(const unsigned& num_inputs = 0, const unsigned& num_neurons = 0):
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layer(const unsigned& num_inputs = 0, const unsigned& num_neurons = 0):
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std::vector<neuron>(num_neurons, neuron(num_inputs)) {}
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void reset()
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@ -169,11 +169,11 @@ namespace mlp {
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for(iterator n = begin(); n != end(); ++n)
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n->reset();
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}
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vector operator()(const vector& input) const
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{
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vector output(size());
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for(unsigned i = 0; i < output.size(); ++i)
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output[i] = (*this)[i](input);
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@ -212,7 +212,7 @@ namespace std {
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copy(l.begin(), l.end(), oi);
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return os;
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}
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istream& operator>>(istream& is, mlp::layer& l)
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{
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for (mlp::layer::iterator li = l.begin() ; li != l.end() ; li++) {
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@ -224,7 +224,7 @@ namespace std {
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}
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namespace mlp {
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//---------------------------------------------------------------------------
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// net
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@ -234,7 +234,7 @@ namespace mlp {
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{
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public:
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net(const unsigned& num_inputs = 0,
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const unsigned& num_outputs = 0,
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const unsigned& num_outputs = 0,
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const std::vector<unsigned>& hidden = std::vector<unsigned>())
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{
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init(num_inputs,num_outputs,hidden);
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@ -272,8 +272,8 @@ namespace mlp {
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assert(c == '>');
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}
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void init( unsigned num_inputs,
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unsigned num_outputs,
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void init( unsigned num_inputs,
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unsigned num_outputs,
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const std::vector<unsigned>& hidden ) {
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clear();
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switch(hidden.size())
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@ -317,21 +317,21 @@ namespace mlp {
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unsigned num_inputs() const { return front().front().length() - 1; }
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unsigned num_outputs() const { return back().size(); }
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unsigned num_hidden_layers() const {
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signed s = (signed) size() -1;
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unsigned num_hidden_layers() const {
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signed s = (signed) size() -1;
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return (s<0) ? 0 : s ;
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}
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unsigned length()
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unsigned length()
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{
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unsigned sum = 0;
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for(iterator l = begin(); l != end(); ++l)
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sum += l->length();
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return sum;
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}
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return sum;
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}
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void normalize()
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{
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@ -356,38 +356,38 @@ namespace mlp {
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vector net::operator()(const vector& input) const
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{
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vector tmp = input;
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for(const_iterator l = begin(); l != end(); ++l)
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tmp = (*l)(tmp);
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return tmp;
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}
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}
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#endif
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//---------------------------------------------------------------------------
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// sample
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//---------------------------------------------------------------------------
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struct sample
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{
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vector input, output;
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sample(unsigned input_size = 0, unsigned output_size = 0):
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input(input_size), output(output_size) {}
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};
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istream& operator>>(istream& is, sample& s)
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{
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return is >> s.input >> s.output;
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}
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ostream& operator<<(ostream& os, const sample& s)
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{
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return os << s.input << " " << s.output;
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}
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//---------------------------------------------------------------------------
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// set
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//---------------------------------------------------------------------------
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@ -395,8 +395,8 @@ namespace mlp {
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class set: public std::vector<sample>
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{
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public:
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set(unsigned input_size = 0, unsigned output_size = 0,
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unsigned num_samples = 0):
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set(unsigned input_size = 0, unsigned output_size = 0,
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unsigned num_samples = 0):
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std::vector<sample>(num_samples, sample(input_size, output_size)) {}
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set(istream& is) : std::vector<sample>(0, sample(0, 0)) {
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@ -433,9 +433,9 @@ namespace mlp {
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{
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real sum = 0;
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for(net::const_reverse_iterator l1 = n1.rbegin(), l2 = n2.rbegin();
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for(net::const_reverse_iterator l1 = n1.rbegin(), l2 = n2.rbegin();
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l1 != n1.rend() && l2 != n2.rend(); ++l1, ++l2)
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for(layer::const_iterator n1 = l1->begin(), n2 = l2->begin();
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for(layer::const_iterator n1 = l1->begin(), n2 = l2->begin();
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n1 != l1->end() && n2 != l2->end(); ++n1, ++n2)
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{
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real b = n1->bias - n2->bias;
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@ -462,6 +462,7 @@ namespace mlp {
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#endif // mlp_h
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// Local Variables:
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// mode:C++
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// Local Variables:
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// mode:C++
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// c-file-style: "Stroustrup"
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// End:
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