machine_learning::adaline Class Reference

Collaboration diagram for machine_learning::adaline:

Public Member Functions
	adaline (int num_features, const double eta=0.01f, const double accuracy=1e-5)
int	predict (const std::vector< double > &x, double *out=nullptr)
double	fit (const std::vector< double > &x, const int &y)
template<int N>
void	fit (std::vector< double > const (&X)[N], const int *y)
int	activation (double x)

Private Member Functions
bool	check_size_match (const std::vector< double > &x)

Private Attributes
const double	eta
	learning rate of the algorithm
const double	accuracy
	model fit convergence accuracy
std::vector< double >	weights
	weights of the neural network

Friends
std::ostream &	operator<< (std::ostream &out, const adaline &ada)

Constructor & Destructor Documentation

adaline()

machine_learning::adaline::adaline ( int  num_features, const double  eta = 0.01f, const double  accuracy = 1e-5  )

inline

Default constructor

Parameters

[in]	num_features	number of features present
[in]	eta	learning rate (optional, default=0.1)
[in]	convergence	accuracy (optional, default= \(1\times10^{-5}\))

        : eta(eta), accuracy(accuracy) {
        if (eta <= 0) {
            std::cerr << "learning rate should be positive and nonzero"
                      << std::endl;
            std::exit(EXIT_FAILURE);
        }
 
        weights = std::vector<double>(
            num_features +
            1);  // additional weight is for the constant bias term
 
        // initialize with random weights in the range [-50, 49]
        for (int i = 0; i < weights.size(); i++) weights[i] = 1.f;
        // weights[i] = (static_cast<double>(std::rand() % 100) - 50);
    }

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Member Function Documentation

check_size_match()

bool machine_learning::adaline::check_size_match ( const std::vector< double > &  x )

inlineprivate

convenient function to check if input feature vector size matches the model weights size

Parameters

[in]

x

fecture vector to check

Returns

true size matches

false size does not match

                                                      {
        if (x.size() != (weights.size() - 1)) {
            std::cerr << __func__ << ": "
                      << "Number of features in x does not match the feature "
                         "dimension in model!"
                      << std::endl;
            return false;
        }
        return true;
    }

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fit() [1/2]

double machine_learning::adaline::fit ( const std::vector< double > &  x, const int &  y  )

inline

Update the weights of the model using supervised learning for one feature vector

Parameters

[in]	x	feature vector
[in]	y	known output value

Returns

correction factor

                                                         {
        if (!check_size_match(x))
            return 0;
 
        /* output of the model with current weights */
        int p = predict(x);
        int prediction_error = y - p;  // error in estimation
        double correction_factor = eta * prediction_error;
 
        /* update each weight, the last weight is the bias term */
        for (int i = 0; i < x.size(); i++) {
            weights[i] += correction_factor * x[i];
        }
        weights[x.size()] += correction_factor;  // update bias
 
        return correction_factor;
    }

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fit() [2/2]

template<int N>

void machine_learning::adaline::fit ( std::vector< double > const (&)  X[N], const int *  y  )

inline

Update the weights of the model using supervised learning for an array of vectors.

Parameters

[in]	X	array of feature vector
[in]	y	known output value for each feature vector

                                                            {
        double avg_pred_error = 1.f;
 
        int iter;
        for (iter = 0; (iter < MAX_ITER) && (avg_pred_error > accuracy);
             iter++) {
            avg_pred_error = 0.f;
 
            // perform fit for each sample
            for (int i = 0; i < N; i++) {
                double err = fit(X[i], y[i]);
                avg_pred_error += std::abs(err);
            }
            avg_pred_error /= N;
 
            // Print updates every 200th iteration
            // if (iter % 100 == 0)
            std::cout << "\tIter " << iter << ": Training weights: " << *this
                      << "\tAvg error: " << avg_pred_error << std::endl;
        }
 
        if (iter < MAX_ITER)
 
            std::cout << "Converged after " << iter << " iterations."
                      << std::endl;
        else
            std::cout << "Did not converge after " << iter << " iterations."
                      << std::endl;
    }

predict()

int machine_learning::adaline::predict ( const std::vector< double > &  x, double *  out = nullptr  )

inline

predict the output of the model for given set of features

Parameters

[in]	x	input vector
[out]	out	optional argument to return neuron output before applying activation function (optional, nullptr to ignore)

Returns

model prediction output

                                                                   {
        if (!check_size_match(x))
            return 0;
 
        double y = weights.back();  // assign bias value
 
        // for (int i = 0; i < x.size(); i++) y += x[i] * weights[i];
        y = std::inner_product(x.begin(), x.end(), weights.begin(), y);
 
        if (out != nullptr)  // if out variable is provided
            *out = y;
 
        return activation(y);  // quantizer: apply ADALINE threshold function
    }

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Friends And Related Function Documentation

operator<<

std::ostream& operator<< ( std::ostream &  out, const adaline &  ada  )

friend

Operator to print the weights of the model

                                                                       {
        out << "<";
        for (int i = 0; i < ada.weights.size(); i++) {
            out << ada.weights[i];
            if (i < ada.weights.size() - 1)
                out << ", ";
        }
        out << ">";
        return out;
    }

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The documentation for this class was generated from the following file:

• machine_learning/adaline_learning.cpp