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update ML documentation and add grouping
This commit is contained in:
Krishna Vedala
parent
9a58248668
commit
797a7d4c73
@ -30,8 +30,15 @@
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#include <stdlib.h>
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#include <time.h>
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/**
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* @addtogroup machine_learning Machine learning algorithms
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* @{
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* @addtogroup adaline Adaline learning algorithm
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* @{
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*/
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/** Maximum number of iterations to learn */
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#define MAX_ITER 500 // INT_MAX
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#define MAX_ADALINE_ITER 500 // INT_MAX
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/** structure to hold adaline model parameters */
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struct adaline
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@ -41,7 +48,8 @@ struct adaline
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int num_weights; /**< number of weights of the neural network */
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};
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#define ACCURACY 1e-5 /**< convergence accuracy \f$=1\times10^{-5}\f$ */
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/** convergence accuracy \f$=1\times10^{-5}\f$ */
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#define ADALINE_ACCURACY 1e-5
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/**
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* Default constructor
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@ -77,7 +85,7 @@ struct adaline new_adaline(const int num_features, const double eta)
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}
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/** delete dynamically allocated memory
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* \param[in] ada model from which the memory is to be freeed.
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* \param[in] ada model from which the memory is to be freed.
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*/
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void delete_adaline(struct adaline *ada)
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{
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@ -91,13 +99,18 @@ void delete_adaline(struct adaline *ada)
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* function](https://en.wikipedia.org/wiki/Heaviside_step_function) <img
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* src="https://upload.wikimedia.org/wikipedia/commons/d/d9/Dirac_distribution_CDF.svg"
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* width="200px"/>
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* @param x activation function input
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* @returns \f$f(x)= \begin{cases}1 & \forall\; x > 0\\ -1 & \forall\; x \le0
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* \end{cases}\f$
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*/
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int activation(double x) { return x > 0 ? 1 : -1; }
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int adaline_activation(double x) { return x > 0 ? 1 : -1; }
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/**
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* Operator to print the weights of the model
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* @param ada model for which the values to print
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* @returns pointer to a NULL terminated string of formatted weights
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*/
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char *get_weights_str(struct adaline *ada)
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char *adaline_get_weights_str(const struct adaline *ada)
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{
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static char out[100]; // static so the value is persistent
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@ -121,7 +134,7 @@ char *get_weights_str(struct adaline *ada)
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* activation function (`NULL` to ignore)
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* \returns model prediction output
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*/
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int predict(struct adaline *ada, const double *x, double *out)
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int adaline_predict(struct adaline *ada, const double *x, double *out)
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{
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double y = ada->weights[ada->num_weights - 1]; // assign bias value
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@ -130,7 +143,8 @@ int predict(struct adaline *ada, const double *x, double *out)
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if (out) // if out variable is not NULL
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*out = y;
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return activation(y); // quantizer: apply ADALINE threshold function
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// quantizer: apply ADALINE threshold function
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return adaline_activation(y);
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}
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/**
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@ -142,10 +156,10 @@ int predict(struct adaline *ada, const double *x, double *out)
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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_sample(struct adaline *ada, const double *x, const int y)
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double adaline_fit_sample(struct adaline *ada, const double *x, const int y)
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{
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/* output of the model with current weights */
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int p = predict(ada, x, NULL);
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int p = adaline_predict(ada, x, NULL);
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int prediction_error = y - p; // error in estimation
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double correction_factor = ada->eta * prediction_error;
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@ -168,19 +182,21 @@ double fit_sample(struct adaline *ada, const double *x, const int y)
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* \param[in] y known output value for each feature vector
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* \param[in] N number of training samples
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*/
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void fit(struct adaline *ada, double **X, const int *y, const int N)
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void adaline_fit(struct adaline *ada, double **X, const int *y, const int N)
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{
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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); iter++)
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for (iter = 0;
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(iter < MAX_ADALINE_ITER) && (avg_pred_error > ADALINE_ACCURACY);
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iter++)
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{
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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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{
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double err = fit_sample(ada, X[i], y[i]);
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double err = adaline_fit_sample(ada, X[i], y[i]);
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avg_pred_error += fabs(err);
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}
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avg_pred_error /= N;
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@ -188,15 +204,19 @@ void fit(struct adaline *ada, double **X, const int *y, const int N)
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// Print updates every 200th iteration
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// if (iter % 100 == 0)
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printf("\tIter %3d: Training weights: %s\tAvg error: %.4f\n", iter,
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get_weights_str(ada), avg_pred_error);
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adaline_get_weights_str(ada), avg_pred_error);
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}
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if (iter < MAX_ITER)
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if (iter < MAX_ADALINE_ITER)
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printf("Converged after %d iterations.\n", iter);
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else
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printf("Did not converged after %d iterations.\n", iter);
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}
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/** @}
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* @}
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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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* \f$x=y\f$ as +1 and others as -1.
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@ -221,19 +241,19 @@ void test1(double eta)
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}
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printf("------- Test 1 -------\n");
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printf("Model before fit: %s", get_weights_str(&ada));
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printf("Model before fit: %s", adaline_get_weights_str(&ada));
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fit(&ada, X, Y, N);
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printf("Model after fit: %s\n", get_weights_str(&ada));
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adaline_fit(&ada, X, Y, N);
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printf("Model after fit: %s\n", adaline_get_weights_str(&ada));
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double test_x[] = {5, -3};
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int pred = predict(&ada, test_x, NULL);
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int pred = adaline_predict(&ada, test_x, NULL);
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printf("Predict for x=(5,-3): % d", pred);
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assert(pred == -1);
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printf(" ...passed\n");
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double test_x2[] = {5, 8};
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pred = predict(&ada, test_x2, NULL);
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pred = adaline_predict(&ada, test_x2, NULL);
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printf("Predict for x=(5, 8): % d", pred);
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assert(pred == 1);
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printf(" ...passed\n");
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@ -275,10 +295,10 @@ void test2(double eta)
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}
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printf("------- Test 2 -------\n");
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printf("Model before fit: %s", get_weights_str(&ada));
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printf("Model before fit: %s", adaline_get_weights_str(&ada));
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fit(&ada, X, Y, N);
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printf("Model after fit: %s\n", get_weights_str(&ada));
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adaline_fit(&ada, X, Y, N);
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printf("Model after fit: %s\n", adaline_get_weights_str(&ada));
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int N_test_cases = 5;
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double test_x[2];
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@ -289,7 +309,7 @@ void test2(double eta)
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test_x[0] = x0;
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test_x[1] = x1;
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int pred = predict(&ada, test_x, NULL);
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int pred = adaline_predict(&ada, test_x, NULL);
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printf("Predict for x=(% 3.2f,% 3.2f): % d", x0, x1, pred);
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int expected_val = (x0 + 3. * x1) > -1 ? 1 : -1;
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@ -343,10 +363,10 @@ void test3(double eta)
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}
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printf("------- Test 3 -------\n");
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printf("Model before fit: %s", get_weights_str(&ada));
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printf("Model before fit: %s", adaline_get_weights_str(&ada));
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fit(&ada, X, Y, N);
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printf("Model after fit: %s\n", get_weights_str(&ada));
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adaline_fit(&ada, X, Y, N);
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printf("Model after fit: %s\n", adaline_get_weights_str(&ada));
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int N_test_cases = 5;
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double test_x[6];
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@ -361,7 +381,7 @@ void test3(double eta)
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test_x[3] = x0 * x0;
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test_x[4] = x1 * x1;
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test_x[5] = x2 * x2;
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int pred = predict(&ada, test_x, NULL);
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int pred = adaline_predict(&ada, test_x, NULL);
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printf("Predict for x=(% 3.2f,% 3.2f): % d", x0, x1, pred);
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int expected_val = (x0 * x0 + x1 * x1 + x2 * x2) <= 1 ? 1 : -1;
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