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from bayes_opt import BayesianOptimization | |
from sklearn.cross_validation import KFold | |
import xgboost as xgb | |
def xgbCv(train, features, numRounds, eta, gamma, maxDepth, minChildWeight, subsample, colSample): | |
# prepare xgb parameters | |
params = { | |
"objective": "reg:linear", | |
"booster" : "gbtree", | |
"eval_metric": "mae", |
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function mpii_convert_json( ) | |
% convert mpii annotations .mat file to .json | |
%% load annotation file | |
fprintf('Load annotations... ') | |
data = load('/media/HDD2/Datasets/Human_Pose/mpii/mpii_human_pose_v1_u12_2/mpii_human_pose_v1_u12_1.mat'); | |
fprintf('Done.\n') | |
%% open file | |
fprintf('Open file mpii_human_pose_annotations.json\n') |