def prepare_training_data_modelz():
meteor_scores = np.array(get_meteor_scores('meteor.output.train'))
test_meteor_scores = np.array(get_meteor_scores('meteor.output.test'))
sts_scores = np.array(get_sts_scores('score.train'))
np.savetxt('x.meteor.train', meteor_scores)
np.savetxt('y.meteor.train', sts_scores)
np.savetxt('x.meteor.test', test_meteor_scores)
def prepare_training_data_modelx():
sts_scores = np.array(get_sts_scores('score.train'))
_x, to_remove = get_asiya_scores()
x = np.array(_x)
y = sts_scores
y =np.delete(y, to_remove, axis=0)
n = len(y)
np.savetxt('x.asiya.train', x)
np.savetxt('y.asiya.train', y)
test_asiya_scores = np.array(get_asiya_test_scores())
np.savetxt('x.asiya.test', test_asiya_scores)
from sklearn.linear_model import ElasticNet, LogisticRegression, RandomizedLogisticRegression
from sklearn.linear_model import PassiveAggressiveRegressor, RANSACRegressor
from sklearn.isotonic import IsotonicRegression
from sklearn import ensemble
from sklearn.svm import SVR
from sklearn.gaussian_process import GaussianProcess
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from sts_data import get_meteor_scores, get_sts_scores
from asiya import get_asiya_scores
meteor_scores = np.array(get_meteor_scores('meteor.output.train'))
sts_scores = np.array(get_sts_scores('score.train'))
test_meteor_scores = np.array(get_meteor_scores('meteor.output.test'))
x = meteor_scores
_x, to_remove = get_asiya_scores()
x = np.array(_x)
y = sts_scores
y = np.delete(y, to_remove, axis=0)
n = len(y)
xt = x
#print len(_x), len(x), len(y)
# Linear Regression
from sklearn.linear_model import ElasticNet, LogisticRegression, RandomizedLogisticRegression
from sklearn.linear_model import PassiveAggressiveRegressor, RANSACRegressor
from sklearn.isotonic import IsotonicRegression
from sklearn import ensemble
from sklearn.svm import SVR
from sklearn.gaussian_process import GaussianProcess
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from sts_data import get_meteor_scores, get_sts_scores
from asiya import get_asiya_scores
meteor_scores = np.array(get_meteor_scores('meteor.output.train'))
sts_scores = np.array(get_sts_scores('score.train'))
test_meteor_scores = np.array(get_meteor_scores('meteor.output.test'))
x = meteor_scores
_x, to_remove = get_asiya_scores()
x = np.array(_x)
y = sts_scores
y =np.delete(y, to_remove, axis=0)
n = len(y)
xt = x
#print len(_x), len(x), len(y)
# Linear Regression
