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https://hub.njuu.cf/TheAlgorithms/C-Plus-Plus.git
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add namespace - machine_learning
This commit is contained in:
Krishna Vedala
parent
e9f795e9c3
commit
579620271f
@ -31,155 +31,164 @@
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#define MAX_ITER 500 // INT_MAX ///< Maximum number of iterations to learn
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#define MAX_ITER 500 // INT_MAX ///< Maximum number of iterations to learn
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class adaline {
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/** \namespace machine_learning
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public:
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* \brief Machine learning algorithms
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/**
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*/
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* Default constructor
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namespace machine_learning {
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* \param[in] num_features number of features present
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class adaline {
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* \param[in] eta learning rate (optional, default=0.1)
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public:
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* \param[in] convergence accuracy (optional, default=\f$1\times10^{-5}\f$)
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/**
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*/
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* Default constructor
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adaline(int num_features, const double eta = 0.01f,
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* \param[in] num_features number of features present
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const double accuracy = 1e-5)
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* \param[in] eta learning rate (optional, default=0.1)
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: eta(eta), accuracy(accuracy) {
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* \param[in] convergence accuracy (optional,
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if (eta <= 0) {
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* default=\f$1\times10^{-5}\f$)
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std::cerr << "learning rate should be positive and nonzero"
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*/
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<< std::endl;
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adaline(int num_features, const double eta = 0.01f,
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std::exit(EXIT_FAILURE);
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const double accuracy = 1e-5)
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}
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: eta(eta), accuracy(accuracy) {
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if (eta <= 0) {
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weights = std::vector<double>(
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std::cerr << "learning rate should be positive and nonzero"
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num_features +
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<< std::endl;
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1); // additional weight is for the constant bias term
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std::exit(EXIT_FAILURE);
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// initialize with random weights in the range [-50, 49]
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for (int i = 0; i < weights.size(); i++) weights[i] = 1.f;
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// weights[i] = (static_cast<double>(std::rand() % 100) - 50);
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}
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/**
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* Operator to print the weights of the model
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*/
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friend std::ostream &operator<<(std::ostream &out, const adaline &ada) {
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out << "<";
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for (int i = 0; i < ada.weights.size(); i++) {
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out << ada.weights[i];
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if (i < ada.weights.size() - 1)
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out << ", ";
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}
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out << ">";
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return out;
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}
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/**
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* predict the output of the model for given set of features
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* \param[in] x input vector
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* \param[out] out optional argument to return neuron output before applying
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* activation function (optional, `nullptr` to ignore)
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* \returns model prediction output
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*/
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int predict(const std::vector<double> &x, double *out = nullptr) {
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if (!check_size_match(x))
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return 0;
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double y = weights.back(); // assign bias value
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// for (int i = 0; i < x.size(); i++) y += x[i] * weights[i];
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y = std::inner_product(x.begin(), x.end(), weights.begin(), y);
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if (out != nullptr) // if out variable is provided
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*out = y;
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return activation(y); // quantizer: apply ADALINE threshold function
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}
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/**
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* Update the weights of the model using supervised learning for one feature
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* vector
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* \param[in] x feature vector
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* \param[in] y known output value
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* \returns correction factor
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*/
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double fit(const std::vector<double> &x, const int &y) {
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if (!check_size_match(x))
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return 0;
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/* output of the model with current weights */
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int p = predict(x);
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int prediction_error = y - p; // error in estimation
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double correction_factor = eta * prediction_error;
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/* update each weight, the last weight is the bias term */
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for (int i = 0; i < x.size(); i++) {
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weights[i] += correction_factor * x[i];
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}
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weights[x.size()] += correction_factor; // update bias
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return correction_factor;
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}
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/**
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* Update the weights of the model using supervised learning for an array of
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* vectors.
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* \param[in] X array of feature vector
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* \param[in] y known output value for each feature vector
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*/
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template <int N>
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void fit(std::vector<double> const (&X)[N], const int *y) {
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double avg_pred_error = 1.f;
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int iter;
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for (iter = 0; (iter < MAX_ITER) && (avg_pred_error > accuracy);
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iter++) {
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avg_pred_error = 0.f;
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// perform fit for each sample
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for (int i = 0; i < N; i++) {
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double err = fit(X[i], y[i]);
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avg_pred_error += std::abs(err);
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}
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}
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avg_pred_error /= N;
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// Print updates every 200th iteration
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weights = std::vector<double>(
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// if (iter % 100 == 0)
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num_features +
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std::cout << "\tIter " << iter << ": Training weights: " << *this
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1); // additional weight is for the constant bias term
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<< "\tAvg error: " << avg_pred_error << std::endl;
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// initialize with random weights in the range [-50, 49]
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for (int i = 0; i < weights.size(); i++) weights[i] = 1.f;
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// weights[i] = (static_cast<double>(std::rand() % 100) - 50);
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}
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}
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if (iter < MAX_ITER)
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/**
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* Operator to print the weights of the model
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std::cout << "Converged after " << iter << " iterations."
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*/
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<< std::endl;
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friend std::ostream &operator<<(std::ostream &out, const adaline &ada) {
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else
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out << "<";
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std::cout << "Did not converge after " << iter << " iterations."
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for (int i = 0; i < ada.weights.size(); i++) {
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<< std::endl;
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out << ada.weights[i];
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}
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if (i < ada.weights.size() - 1)
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out << ", ";
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int activation(double x) { return x > 0 ? 1 : -1; }
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}
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out << ">";
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private:
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return out;
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/**
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* convenient function to check if input feature vector size matches the
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* model weights size
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* \param[in] x fecture vector to check
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* \returns `true` size matches
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* \returns `false` size does not match
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*/
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bool check_size_match(const std::vector<double> &x) {
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if (x.size() != (weights.size() - 1)) {
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std::cerr << __func__ << ": "
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<< "Number of features in x does not match the feature "
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"dimension in model!"
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<< std::endl;
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return false;
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}
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}
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return true;
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}
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const double eta; ///< learning rate of the algorithm
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/**
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const double accuracy; ///< model fit convergence accuracy
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* predict the output of the model for given set of features
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std::vector<double> weights; ///< weights of the neural network
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* \param[in] x input vector
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};
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* \param[out] out optional argument to return neuron output before
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* applying activation function (optional, `nullptr` to ignore) \returns
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* model prediction output
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*/
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int predict(const std::vector<double> &x, double *out = nullptr) {
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if (!check_size_match(x))
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return 0;
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double y = weights.back(); // assign bias value
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// for (int i = 0; i < x.size(); i++) y += x[i] * weights[i];
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y = std::inner_product(x.begin(), x.end(), weights.begin(), y);
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if (out != nullptr) // if out variable is provided
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*out = y;
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return activation(
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y); // quantizer: apply ADALINE threshold function
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}
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/**
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* Update the weights of the model using supervised learning for one
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* feature vector \param[in] x feature vector \param[in] y known output
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* value \returns correction factor
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*/
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double fit(const std::vector<double> &x, const int &y) {
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if (!check_size_match(x))
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return 0;
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/* output of the model with current weights */
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int p = predict(x);
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int prediction_error = y - p; // error in estimation
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double correction_factor = eta * prediction_error;
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/* update each weight, the last weight is the bias term */
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for (int i = 0; i < x.size(); i++) {
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weights[i] += correction_factor * x[i];
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}
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weights[x.size()] += correction_factor; // update bias
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return correction_factor;
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}
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/**
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* Update the weights of the model using supervised learning for an
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* array of vectors. \param[in] X array of feature vector \param[in] y
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* known output value for each feature vector
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*/
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template <int N>
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void fit(std::vector<double> const (&X)[N], const int *y) {
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double avg_pred_error = 1.f;
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int iter;
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for (iter = 0; (iter < MAX_ITER) && (avg_pred_error > accuracy);
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iter++) {
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avg_pred_error = 0.f;
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// perform fit for each sample
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for (int i = 0; i < N; i++) {
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double err = fit(X[i], y[i]);
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avg_pred_error += std::abs(err);
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}
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avg_pred_error /= N;
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// Print updates every 200th iteration
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// if (iter % 100 == 0)
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std::cout << "\tIter " << iter
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<< ": Training weights: " << *this
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<< "\tAvg error: " << avg_pred_error << std::endl;
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}
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if (iter < MAX_ITER)
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std::cout << "Converged after " << iter << " iterations."
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<< std::endl;
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else
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std::cout << "Did not converge after " << iter << " iterations."
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<< std::endl;
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}
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int activation(double x) { return x > 0 ? 1 : -1; }
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private:
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/**
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* convenient function to check if input feature vector size matches the
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* model weights size
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* \param[in] x fecture vector to check
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* \returns `true` size matches
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* \returns `false` size does not match
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*/
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bool check_size_match(const std::vector<double> &x) {
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if (x.size() != (weights.size() - 1)) {
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std::cerr
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<< __func__ << ": "
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<< "Number of features in x does not match the feature "
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"dimension in model!"
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<< std::endl;
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return false;
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}
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return true;
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}
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const double eta; ///< learning rate of the algorithm
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const double accuracy; ///< model fit convergence accuracy
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std::vector<double> weights; ///< weights of the neural network
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};
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} // namespace machine_learning
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using machine_learning::adaline;
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/**
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/**
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* test function to predict points in a 2D coordinate system above the line
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* test function to predict points in a 2D coordinate system above the line
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