def main(): tco_dir = os.path.join(os.getenv("SIGVISA_HOME"), "papers", "product_tree", "tco_learned") # gen_tco(tco_dir) tco_train_X, tco_train_y, tco_test_X, tco_test_y = load_tco(tco_dir=tco_dir) # hyperparams = learn_hyperparams(tco_dir, tco_train_X, tco_train_y, hyperparams=np.array([1, 100, 100, 100], dtype=float), dfn_str='lld', sparse_invert=True, k=1000) hyperparams = np.array([95.140702, 12552.9422512, 1257.77376988, 100.0], dtype=float) # hyperparams = np.array([95.140702, 12552.9422512, 100.0, 100.], dtype=float) np.save(os.path.join(tco_dir, "testX.npy"), tco_test_X) np.save(os.path.join(tco_dir, "testy.npy"), tco_test_y) np.save(os.path.join(tco_dir, "hyperparams.npy"), hyperparams) print "loaded data" train_realdata_gp( tco_dir, tco_train_X, tco_train_y, hyperparams=hyperparams, dfn_str="lld", sparse_invert=False, basisfns=[lambda x: 1], param_cov=np.array(((10000,),)), param_mean=np.array((0,)), ) print "trained model" test_predict(tco_dir) print "evaluated predictions" # eval_gp(bdir=tco_dir, test_n=100) print "timings finished"
def main(): sarcos_dir = os.path.join(os.getenv("SIGVISA_HOME"), "papers", "product_tree", "sarcos") sarcos_train_X, sarcos_train_y, sarcos_test_X, sarcos_test_y, hyperparams = load_sarcos(sdir=sarcos_dir) np.save(os.path.join(sarcos_dir, "testX.npy"), sarcos_test_X) np.save(os.path.join(sarcos_dir, "testy.npy"), sarcos_test_y) np.save(os.path.join(sarcos_dir, "hyperparams.npy"), hyperparams) print "loaded sarcos data and converted to numpy format" train_realdata_gp(sarcos_dir, sarcos_train_X, sarcos_train_y, hyperparams) print "trained model" test_predict(sarcos_dir) print "evaluated predictions" eval_gp(bdir=sarcos_dir, test_n=100) print "timings finished"
def learn_gp(X, y): p = np.random.permutation(len(y)) train_n = int(len(y) * 0.2) trainX = X[p[:train_n], :] trainy = y[p[:train_n]] testX = X[p[train_n:], :] testy = y[p[train_n:]] fitz_dir = os.path.join(os.getenv("SIGVISA_HOME"), "papers", "product_tree", "fitz_learned") # hyperparams = np.array([0.5, 3.0, 50.0, 50.0], dtype=float) # hyperparams = learn_hyperparams(fitz_dir, trainX, trainy, dfn_str='lld', hyperparams=hyperparams, sparse_invert=False, basisfns = [lambda x : 1,], param_cov=np.array(((10000,),)), param_mean = np.array((0,)), k=1000) # print "got hyperparams", hyperparams # hyperparams = np.array([1.16700753, 2.53145332, 212.46536884,157.68719303], dtype=float) np.save(os.path.join(fitz_dir, "testX.npy"), testX) np.save(os.path.join(fitz_dir, "testy.npy"), testy) np.save(os.path.join(fitz_dir, "hyperparams.npy"), hyperparams) print "loaded data" train_realdata_gp( fitz_dir, trainX, trainy, hyperparams=hyperparams, sparse_invert=False, basisfns=[lambda x: 1], param_cov=np.array(((10000,),)), param_mean=np.array((0,)), dfn_str="lld", ) test_predict(fitz_dir) eval_gp(bdir=fitz_dir, test_n=100)