def opt_run():
param_grid = [{'C': [1, 10, 100, 1000]}]
clf = linear_model.LogisticRegression()
grid_search = GridSearchCV(clf, cv=16, param_grid=param_grid)
XX, Y = sensors_data.read_data(0)
grid_search.fit(XX, Y)
report(grid_search)
def run():
if not os.path.isdir(config.model_logreg_folder):
os.mkdir(config.model_logreg_folder)
for i in range(120):
XX, Y = sensors_data.read_data(i)
clf = linear_model.LogisticRegression()
scores = cross_validation.cross_val_score(clf, XX, Y, cv=16)
save_model(clf, 'c1', i)
print str(i) + '\t' + str(scores.mean()) + '\t' + str(scores.std())
sys.stdout.flush()
