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add namespace - machine_learning

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This commit is contained in:
Krishna Vedala 2020-05-31 23:00:38 -04:00
parent e9f795e9c3
commit 579620271f
1 changed files with 152 additions and 143 deletions
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41
machine_learning/adaline_learning.cpp
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@ -31,13 +31,18 @@
#define MAX_ITER 500 // INT_MAX ///< Maximum number of iterations to learn #define MAX_ITER 500 // INT_MAX ///< Maximum number of iterations to learn
/** \namespace machine_learning
* \brief Machine learning algorithms
*/
namespace machine_learning {
class adaline { class adaline {
public: public:
/** /**
* Default constructor * Default constructor
* \param[in] num_features number of features present * \param[in] num_features number of features present
* \param[in] eta learning rate (optional, default=0.1) * \param[in] eta learning rate (optional, default=0.1)
* \param[in] convergence accuracy (optional, default=\f$1\times10^{-5}\f$) * \param[in] convergence accuracy (optional,
* default=\f$1\times10^{-5}\f$)
*/ */
adaline(int num_features, const double eta = 0.01f, adaline(int num_features, const double eta = 0.01f,
const double accuracy = 1e-5) const double accuracy = 1e-5)
@ -74,9 +79,9 @@ class adaline {
/** /**
* predict the output of the model for given set of features * predict the output of the model for given set of features
* \param[in] x input vector * \param[in] x input vector
* \param[out] out optional argument to return neuron output before applying * \param[out] out optional argument to return neuron output before
* activation function (optional, `nullptr` to ignore) * applying activation function (optional, `nullptr` to ignore) \returns
* \returns model prediction output * model prediction output
*/ */
int predict(const std::vector<double> &x, double *out = nullptr) { int predict(const std::vector<double> &x, double *out = nullptr) {
if (!check_size_match(x)) if (!check_size_match(x))
@ -90,15 +95,14 @@ class adaline {
if (out != nullptr) // if out variable is provided if (out != nullptr) // if out variable is provided
*out = y; *out = y;
return activation(y); // quantizer: apply ADALINE threshold function return activation(
y); // quantizer: apply ADALINE threshold function
} }
/** /**
* Update the weights of the model using supervised learning for one feature * Update the weights of the model using supervised learning for one
* vector * feature vector \param[in] x feature vector \param[in] y known output
* \param[in] x feature vector * value \returns correction factor
* \param[in] y known output value
* \returns correction factor
*/ */
double fit(const std::vector<double> &x, const int &y) { double fit(const std::vector<double> &x, const int &y) {
if (!check_size_match(x)) if (!check_size_match(x))
@ -119,10 +123,9 @@ class adaline {
} }
/** /**
* Update the weights of the model using supervised learning for an array of * Update the weights of the model using supervised learning for an
* vectors. * array of vectors. \param[in] X array of feature vector \param[in] y
* \param[in] X array of feature vector * known output value for each feature vector
* \param[in] y known output value for each feature vector
*/ */
template <int N> template <int N>
void fit(std::vector<double> const (&X)[N], const int *y) { void fit(std::vector<double> const (&X)[N], const int *y) {
@ -142,7 +145,8 @@ class adaline {
// Print updates every 200th iteration // Print updates every 200th iteration
// if (iter % 100 == 0) // if (iter % 100 == 0)
std::cout << "\tIter " << iter << ": Training weights: " << *this std::cout << "\tIter " << iter
<< ": Training weights: " << *this
<< "\tAvg error: " << avg_pred_error << std::endl; << "\tAvg error: " << avg_pred_error << std::endl;
} }
@ -167,7 +171,8 @@ class adaline {
*/ */
bool check_size_match(const std::vector<double> &x) { bool check_size_match(const std::vector<double> &x) {
if (x.size() != (weights.size() - 1)) { if (x.size() != (weights.size() - 1)) {
std::cerr << __func__ << ": " std::cerr
<< __func__ << ": "
<< "Number of features in x does not match the feature " << "Number of features in x does not match the feature "
"dimension in model!" "dimension in model!"
<< std::endl; << std::endl;
@ -181,6 +186,10 @@ class adaline {
std::vector<double> weights; ///< weights of the neural network std::vector<double> weights; ///< weights of the neural network
}; };
} // namespace machine_learning
using machine_learning::adaline;
/** /**
* test function to predict points in a 2D coordinate system above the line * test function to predict points in a 2D coordinate system above the line
* \f$x=y\f$ as +1 and others as -1. * \f$x=y\f$ as +1 and others as -1.