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src/d/r/dragnet-HEAD/dragnet/model_training.py   dragnet(Download)
    from .blocks import TagCountBlockifier as Blkr
 
    from mozsci.models import LogisticRegression
    from mozsci.numpy_util import NumpyEncoder
 
    # train the model
    print "Training the model"
    model = LogisticRegression(lam=lam)
    features, labels, weights = trainer.make_features_from_data(data,
                         model_to_train, training_or_test='training')

src/d/r/dragnet-HEAD/dragnet/__init__.py   dragnet(Download)
# and DragnetModel until existing
# code can be updated to use the new interface
from mozsci.models import LogisticRegression as lr
class LogisticRegression(lr):
    def pred(self, *args, **kwargs):

src/m/o/mozsci-HEAD/test/test_map_train.py   mozsci(Download)
from mozsci.evaluation import classification_error, auc_wmw_fast
from mozsci.cross_validate import cv_kfold
from mozsci.models import LogisticRegression
 
 

src/m/o/mozsci-HEAD/test/test_logistic_regression.py   mozsci(Download)
 
import unittest
from mozsci.models import LogisticRegression
import numpy as np
 
        x = np.random.rand(N, 2)
        y = (3 * x[:, 0] - 2 * x[:, 1] - 1.5 > 0.0).astype(np.int)
        lr = LogisticRegression()
        lr.fit(x, y, factr=1e4)
        ypred = lr.predict(x)

src/d/r/dragnet-HEAD/test/test_kohlschuetter.py   dragnet(Download)
import re
import numpy as np
from mozsci.models import LogisticRegression
from html_for_testing import big_html_doc
 

src/d/r/dragnet-HEAD/test/test_content_extraction_model.py   dragnet(Download)
import re
import numpy as np
from mozsci.models import LogisticRegression
from html_for_testing import big_html_doc