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summary(c[2])
      freq
 Min.   :   4.0
 1st Qu.: 521.0
 Median : 634.0
 Mean   : 632.3
 3rd Qu.: 747.0
 Max.   :1215.0

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aggregate(data$age, by=list(data$group), FUN=mean)[2]

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df2 <- subset(df, article_id %in% names(tt[tt > 200]))

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0.863635408019
{'estimator__kernel': 'rbf', 'estimator__C': 8, 'estimator__gamma': 0.00048828125}

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{'estimator__kernel': 'rbf', 'estimator__C': 8, 'estimator__degree': 3.0517578125e-05}

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from sklearn.datasets import load_iris
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import f1_score

model_to_set = OneVsRestClassifier(SVC())

parameters = {
    "estimator__C": [2e-5, 2e-3, 2e-1, 2e1, 2e3, 2e5, 2e7, 2e9, 2e11, 2e13, 2e15],

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mean = []
for bid, loc in bid_loc.iteritems():
    try:
        mean.append(np.mean(pid_ftr[loc],axis=0))
    except IndexError:
        pass

np.save('mean_vector', mean)

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bid_loc = defaultdict(list)
i = 0
for key, value in bid_img.iteritems():
    for v in value:
        bid_loc[key].append(idx[i])
        i += 1

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t = time.time()
idx = []
for key, value in bid_img.iteritems():
    for v in value:
        loc = -1
        for pid in pid_lst:
            loc += 1
            if v == pid:
                idx.append(loc)    

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i = 0
for x in y_train:
    i += 1
    if not x:
        print i