Python getFuncs примеры использования

Пример #1

Файл: 36-whichBest_andScatter_RL.py Проект: jameshughes89/NonlinearModelsFMRI-2

import bestExpressions_L_FDR_RELATIONAL_LASSO_1 as relational_FDR_LASSO
import bestExpressions_L_FDR_SOCIAL_LASSO_1 as social_FDR_LASSO
import bestExpressions_L_FDR_WM_LASSO_1 as wm_FDR_LASSO

functions_FDR_LASSO = [emotion_FDR_LASSO.getFuncs(), gambling_FDR_LASSO.getFuncs(), language_FDR_LASSO.getFuncs(), motor_FDR_LASSO.getFuncs(), relational_FDR_LASSO.getFuncs(), social_FDR_LASSO.getFuncs(), wm_FDR_LASSO.getFuncs()]

# TOP 30
import bestExpressions_L_TOP30_EMOTION as emotion_TOP30	
import bestExpressions_L_TOP30_GAMBLING as gambling_TOP30	
import bestExpressions_L_TOP30_LANGUAGE as language_TOP30		
import bestExpressions_L_TOP30_MOTOR as motor_TOP30		
import bestExpressions_L_TOP30_RELATIONAL as relational_TOP30
import bestExpressions_L_TOP30_SOCIAL as social_TOP30
import bestExpressions_L_TOP30_WM as wm_TOP30

functions_TOP30 = [emotion_TOP30.getFuncs(), gambling_TOP30.getFuncs(), language_TOP30.getFuncs(), motor_TOP30.getFuncs(), relational_TOP30.getFuncs(), social_TOP30.getFuncs(), wm_TOP30.getFuncs()]

import bestExpressions_L_TOP30_EMOTION_LASSO_1 as emotion_TOP30_LASSO
import bestExpressions_L_TOP30_GAMBLING_LASSO_1 as gambling_TOP30_LASSO	
import bestExpressions_L_TOP30_LANGUAGE_LASSO_1 as language_TOP30_LASSO
import bestExpressions_L_TOP30_MOTOR_LASSO_1 as motor_TOP30_LASSO
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

Пример #2

]

# TOP 30
import bestExpressions_L_TOP30_EMOTION as emotion_TOP30
import bestExpressions_L_TOP30_GAMBLING as gambling_TOP30
import bestExpressions_L_TOP30_LANGUAGE as language_TOP30
import bestExpressions_L_TOP30_MOTOR as motor_TOP30
import bestExpressions_L_TOP30_RELATIONAL as relational_TOP30
import bestExpressions_L_TOP30_SOCIAL as social_TOP30
import bestExpressions_L_TOP30_WM as wm_TOP30

functions_TOP30 = [
    emotion_TOP30.getFuncs(),
    gambling_TOP30.getFuncs(),
    language_TOP30.getFuncs(),
    motor_TOP30.getFuncs(),
    relational_TOP30.getFuncs(),
    social_TOP30.getFuncs(),
    wm_TOP30.getFuncs()
]

import bestExpressions_L_TOP30_EMOTION_LASSO_1 as emotion_TOP30_LASSO
import bestExpressions_L_TOP30_GAMBLING_LASSO_1 as gambling_TOP30_LASSO
import bestExpressions_L_TOP30_LANGUAGE_LASSO_1 as language_TOP30_LASSO
import bestExpressions_L_TOP30_MOTOR_LASSO_1 as motor_TOP30_LASSO
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(),

Пример #3

Файл: 8-genLinModelDistMat-TOP30.py Проект: jameshughes89/NonlinearModelsFMRI-2

]
lasts = ["7", "2", "16", "21", "28", "3", "21"]
#subjects =[100307, 100408, 101006, 101107, 101309, 101410, 101915, 102008, 102311, 102816, 103111, 103414, 103515, 103818, 104012, 104820, 105014, 105115, 105216, 105923, 106016, 106319, 106521, 107321, 107422, 108121, 108323, 108525, 108828, 109123, 109325, 110411, 111312, 111413, 111514, 111716, 112819, 113215, 113619, 113821, 113922, 114419, 114924, 115320, 116524, 117122, 117324, 118528, 118730, 118932, 119833, 120111, 120212, 120515, 121315, 121618, 122317, 122620, 123117, 123420, 123925, 124220, 124422, 124826, 125525, 126325, 126628, 127630, 127933, 128127, 128632, 129028, 130013, 130316, 130922, 131217, 131722, 131924, 132118, 133019, 133625, 133827, 133928, 134324, 135225, 135528, 135932, 136227, 136833, 137027, 137128, 137633, 137936, 138231, 138534, 139233, 139637, 140117, 140824, 140925, 141422, 141826, 142424, 142626, 142828, 143325, 144226, 144832, 145531, 145834]

subjects = [
    100307, 100408, 101006, 101107, 101309, 101410, 101915, 102008, 102311,
    102816, 103111, 103414, 103515, 103818, 104012, 104820, 105014, 105115,
    105216, 105923, 106016, 106319, 106521, 107321, 107422, 108121, 108323,
    108525, 108828, 109123, 109325, 110411, 111312, 111413, 111514, 111716,
    113215, 113619, 113922, 114419
]

#all_functions_line = funcsL_EMOTION[:len(subjects)] + funcsL_GAMBLING[:len(subjects)] + funcsL_LANGUAGE[:len(subjects)] + funcsL_MOTOR[:len(subjects)] + funcsL_RELATIONAL[:len(subjects)] + funcsL_SOCIAL[:len(subjects)] + funcsL_WM[:len(subjects)]

all_functions_line = emotion.getFuncs() + gambling.getFuncs(
) + language.getFuncs() + motor.getFuncs() + relational.getFuncs(
) + social.getFuncs() + wm.getFuncs()

matrixMSE = []
matrixABE = []

matrixMIN = []

lastsCount = 0

for t in tasks:
    fs = 'funcsL_' + t + ' = ['
    count = 0
    for s in subjects:
        print t, s
        ALL = []