/**
* @brief Predict from the trendfilter fit at new values of x.
*
* @param x the original data positions used in the fit; length n
* @param beta the beta vector for the prediction
* @param n number of observations
* @param k polynomial degree of the fitted trend
* @param family family of the fit
* @param x0 the new positions to predict at
* @param n0 the number of observations in x0
* @param pred allocated space for the predicted values
* @param zero_tol tolerance for rounding a basis coefficient to zero
* @return void
* @note The results will not be valid unless the values in x0 are within the range
* of the original x inputs.
*/
void tf_predict(double * x, double * beta, int n, int k, int family,
double * x0, int n0, double * pred, double zero_tol)
{
int i;
double f;
tf_predict_gauss(x, beta, n, k, x0, n0, pred, zero_tol);
switch (family)
{
case FAMILY_GAUSSIAN:
break;
case FAMILY_LOGISTIC:
for (i = 0; i < n0; i++)
{
pred[i] = logi_b1(pred[i]);
}
break;
case FAMILY_POISSON:
for (i = 0; i < n0; i++)
{
pred[i] = pois_b1(pred[i]);
}
break;
}
}
void test_admm_gauss(int mode)
{
int i;
double * y;
double * x;
double * w;
int n;
int k;
int family;
int max_iter;
int lam_flag;
int obj_flag;
double * lambda;
int nlambda;
double lambda_min_ratio;
double * beta;
double * pred;
double * obj;
int * iter;
double rho;
double obj_tol;
double predict_zero_tol;
/* Input parameters; will usually be given by parent function to tf_admm */
n = 8;
k = 1;
family = FAMILY_GAUSSIAN;
max_iter = 5;
lam_flag = 1;
obj_flag = 1;
nlambda = 1;
lambda_min_ratio = 1e-4;
rho = 1;
obj_tol = 1e-12;
y = (double *) malloc(n * sizeof(double));
x = (double *) malloc(n * sizeof(double));
w = (double *) malloc(n * sizeof(double));
lambda = (double *) malloc(nlambda * sizeof(double));
beta = (double *) malloc(n * nlambda * sizeof(double));
pred = (double *) malloc(n * sizeof(double));
obj = (double *) malloc(max_iter * nlambda * sizeof(double));
iter = (int *) malloc( nlambda * sizeof(int));
srand(5489);
x[0] = 0;
for (i = 1; i < n; i++) x[i] = x[i-1] + (rand() % 100)/100.;
for (i = 0; i < n; i++) y[i] = x[i] * x[i] + 0.5 * (rand() % 100)/100.;
for (i = 0; i < n; i++) w[i] = 0;
lambda[0] = 1;
/* Call the tf_admm function */
tf_admm(y, x, w, n, k, family, max_iter, lam_flag, obj_flag,
lambda, nlambda, lambda_min_ratio, beta, obj, iter, rho, obj_tol);
printf("\n--------------- (x,y) ---------------------------\n");
for (i = 0; i < n; i++) printf("%.3f\t%1.f\n", x[i], y[i]);
printf("\n---------- lambda -------------------------------\n");
for (i = 0; i < nlambda; i++) printf("%f\n", lambda[i]);
printf("\n---------- beta_1 -------------------------------\n");
for (i = 0; i < n; i++) printf("%f\n", beta[i]);
/*
printf("\n---------- beta_2 -------------------------------\n");
for (i = 0; i < n; i++) printf("%f\n", beta[i + n]);
printf("\n---------- beta_3 -------------------------------\n");
for (i = 0; i < n; i++) printf("%f\n", beta[i + n*2]);
*/
predict_zero_tol = 1e-12;
double err;
for(i = 0; i < nlambda; i++)
{
int offset = i*n;
tf_predict_gauss(beta + offset, x, n, k, x, n, pred, predict_zero_tol);
err = max_diff(beta + offset, pred, n);
printf("Prediction difference at input points=%E\n", err);
if(!(err < 1e-12 ))
printf("Prediction failed at input points (n=%d,k=%d,lam=%.4f,err=%E)\n",n,k,lambda[i], err);
}
/* Logistic loss */
family = FAMILY_LOGISTIC;
double prob1, prob2;
for (i = 0; i < n; i++)
{
prob1 = 1/ ( 1 + exp(-x[i]) );
prob2 = (rand() % 101)/100.;
y[i] = prob1 <= prob2 ? 1 : 0;
}
printf("\n--------------- (x,y) ---------------------------\n");
for (i = 0; i < n; i++) printf("%.3f\t%1.f\n", x[i], y[i]);
lambda[0] = 1e9;
tf_admm(y, x, w, n, k, family, max_iter, lam_flag, obj_flag,
lambda, nlambda, lambda_min_ratio, beta, obj, iter, rho, obj_tol);
printf("\n---------- lambda -------------------------------\n");
for (i = 0; i < nlambda; i++) printf("%f\n", lambda[i]);
printf("\n---------- beta_1 (logistic) -----------------------\n");
for (i = 0; i < n; i++) printf("%f\n", beta[i]);
/* Free the allocated arrays */
free(y);
free(x);
free(w);
free(lambda);
free(beta);
free(obj);
free(iter);
free(pred);
}