def error(program: Dag) -> np.array: errors = [] for ndx, (root, filenames) in enumerate(x_train): y_true = y_train[ndx] n_errors = len(y_true) + 1 if program is None: errors += [penalty] * n_errors else: try: y_pred = program.eval(root=root, filenames=filenames) except Exception as e: # print(e) errors += [penalty] * n_errors continue # Compare the outputs element-wise and compute absolute errors. for ndx, el_pred in enumerate(y_pred[:len(y_true)]): el_true = y_true[ndx] errors.append( damerau_levenshtein_distance(el_true.path_str, el_pred.path_str)) # If y_pred is shorter than y_true, fill in missing elements with penalties. for _ in range(max(0, len(y_true) - len(y_pred))): errors.append(penalty) # Add the difference in size as an error. errors.append(abs(len(y_true) - len(y_pred))) return np.array(errors)
def error(program: Dag) -> np.array: errors = [] for case in x_train: y_true = target(case) if program is None: errors.append(penalty) else: try: y_pred = program.eval(lst=case) errors.append(abs(y_true - y_pred)) except Exception as e: # print(e) errors.append(penalty) return np.array(errors)
def error(program: Dag) -> np.array: errors = [] for i, dec in x_train: if program is None: errors.append(penalty) else: y_true = target(i, dec) try: y_pred = program.eval(i=i, dec=dec) errors.append(abs(y_true - y_pred)) except Exception as e: # print(e) errors.append(penalty) return np.array(errors)
def error(program: Dag) -> np.array: errors = [] for dt1, dt2, y_true in X_train: if program is None: errors.append(penalty) else: try: y_pred = program.eval(dt1=dt1, dt2=dt2) errors.append(abs(y_pred - y_true)) except Exception as e: # print(e) errors.append(penalty) continue return np.array(errors)
def error(program: Dag) -> np.array: errors = [] for x1, x2 in x_train: if program is None: errors.append(penalty) errors.append(penalty) else: y_true = target(x1, x2) try: y_pred = program.eval(x1=float(x1), x2=float(x2)) errors.append(float(not isinstance(y_pred, float))) errors.append(float((y_true - y_pred)**2)) except Exception as e: # print(e) errors.append(penalty) errors.append(penalty) return np.array(errors)
def error(program: Dag) -> np.array: errors = [] for i in x_train: if program is None: errors.append(penalty) errors.append(penalty) else: y_true = target(i) try: y_pred = program.eval(i=i) errors.append(float(not isinstance(y_pred, bool))) errors.append(float(not (bool(y_pred) == y_true))) except Exception as e: # print(e) errors.append(penalty) errors.append(penalty) return np.array(errors)
def error(program: Dag) -> np.array: errors = [] for lst, lower, upper, y_true in X_train: n_errors = len(y_true) + 1 if program is None: errors += [penalty] * n_errors else: try: y_pred = program.eval(lst=lst, lower=lower, upper=upper) except Exception as e: # print(e) errors += [penalty] * n_errors continue # Compare the outputs element-wise and compute absolute errors. for ndx, el_pred in enumerate(y_pred[:len(y_true)]): el_true = y_true[ndx] errors.append(int(el_true != el_pred)) # If y_pred is shorter than y_true, fill in missing elements with penalties. for _ in range(max(0, len(y_true) - len(y_pred))): errors.append(penalty) # Add the difference in size as an error. errors.append(abs(len(y_true) - len(y_pred))) return np.array(errors)