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"]