wm_TOP30_LASSO.getFuncs() ] # NL import allExpressions_NL_EMOTION as emotion_NL import allExpressions_NL_GAMBLING as gambling_NL import allExpressions_NL_LANGUAGE as language_NL import allExpressions_NL_MOTOR as motor_NL import allExpressions_NL_RELATIONAL as relational_NL import allExpressions_NL_SOCIAL as social_NL import allExpressions_NL_WM as wm_NL functions_NL = [ emotion_NL.getFuncs(), gambling_NL.getFuncs(), language_NL.getFuncs(), motor_NL.getFuncs(), relational_NL.getFuncs(), social_NL.getFuncs(), wm_NL.getFuncs() ] def calcFunctionError(f, data): allErrors = [] for l in data: try: allErrors.append(abs(l[-1] - f(*l))) except Exception: allErrors.append(float('nan')) if allErrors[-1] > 100:
import bestExpressions_L_TOP30_RELATIONAL_LASSO_1 as relational_TOP30_LASSO import bestExpressions_L_TOP30_SOCIAL_LASSO_1 as social_TOP30_LASSO import bestExpressions_L_TOP30_WM_LASSO_1 as wm_TOP30_LASSO functions_TOP30_LASSO = [emotion_TOP30_LASSO.getFuncs(), gambling_TOP30_LASSO.getFuncs(), language_TOP30_LASSO.getFuncs(), motor_TOP30_LASSO.getFuncs(), relational_TOP30_LASSO.getFuncs(), social_TOP30_LASSO.getFuncs(), wm_TOP30_LASSO.getFuncs()] # NL import allExpressions_NL_EMOTION as emotion_NL import allExpressions_NL_GAMBLING as gambling_NL import allExpressions_NL_LANGUAGE as language_NL import allExpressions_NL_MOTOR as motor_NL import allExpressions_NL_RELATIONAL as relational_NL import allExpressions_NL_SOCIAL as social_NL import allExpressions_NL_WM as wm_NL functions_NL = [emotion_NL.getFuncs(), gambling_NL.getFuncs(), language_NL.getFuncs(), motor_NL.getFuncs(), relational_NL.getFuncs(), social_NL.getFuncs(), wm_NL.getFuncs()] def calcFunctionError(f, data): allErrors = [] for l in data: try: allErrors.append(abs(l[-1] - f(*l))) except Exception: allErrors.append(float('nan')) if allErrors[-1] > 100: del allErrors[-1] return np.nanmean(allErrors) tasks = ["EMOTION", "GAMBLING", "LANGUAGE", "MOTOR", "RELATIONAL", "SOCIAL", "WM"]