def synthetic_run_params(m, w, n, p, gamma, sigma_step, sigma, tol_cg,
MAXIT_cg, DENSITY, power):
# Data LOAD
X_train, y_train, X_test, y_test, w_true = gen_data.gen_data_SVM(
m, n, p, DENSITY)
m, N = np.shape(X_train)
n = 2 * N + 1
# form l1-SVM constraint matrix
A, b, c = svm_func.formL1SVM(X_train, y_train)
# IPM Standard
t_stan_1 = time.time()
x, y, s, iter_out_stan, iter_in_cg_vec_stan, kap_AD_vec, time_ls_stan = ipm_func.ipm_standard(
m, n, A, b, c, tol_cg)
t_stan_2 = time.time()
# IPM Ours
t_ipm_1 = time.time()
x, y, s, iter_out_ipm, iter_in_cg_vec_ipm, kap_ADW_vec, v_vec, time_ls_ipm = ipm_func.ipm(
m, n, w, A, b, c, tol_cg)
t_ipm_2 = time.time()
results = dict()
results['ipm_time'] = t_ipm_2 - t_ipm_1
results['stan_time'] = t_stan_2 - t_stan_1
results['niter_out_stan'] = iter_out_stan
results['niter__out_ipm'] = iter_out_ipm
results['niter_inner_stan'] = iter_in_cg_vec_stan
results['niter_inner_cg'] = iter_in_cg_vec_ipm
results['inner_cg_vec'] = iter_in_cg_vec_stan
results['kap_AD_vec'] = kap_AD_vec
results['kap_ADW_vec'] = kap_ADW_vec
results['v_vec'] = v_vec
return results
# Sketched IPM vs Standard IPM, 1 Run, number of iterations, condition number
#------------------------------------------------------------
directory = '/Users/grego/GitProjects/SketchIPM_python/ipm_figures'
#------------------------------------------------------------
if __name__ == '__main__':
print('---------------------------', '\n')
print('Test IPM SVM real or synthetic dataset, 1 Run', '\n')
#------------------------------------------------------------
#------------------------------------------------------------
# Data LOAD
t_data_1 = time.time()
#X_train, y_train, X_test, y_test, w_true = gen_data.gen_data_1
X_train, y_train, X_test, y_test, w_true = gen_data.gen_data_SVM(
m, n, p, DENSITY)
#X_train,y_train,X_test,y_test = load_real_data.load_ARCENE()
#X_train,y_train,X_test,y_test = load_real_data.load_DEXTER()
#X_train,y_train = load_real_data.load_DrivFace() # 606 x 6400
#X_train,y_train,X_test,y_test = load_real_data.load_DOROTHEA() # (800 x 100k)
t_data_2 = time.time()
print('time data load = ', t_data_2 - t_data_1, ' secs')
data_desc = 's' # s for synthetic
#data_desc = 'ARCENE'
print('data_desc = ', data_desc)
#------------------------------------------------------------
# Parameters (for real data)