def predict_constant(filename):
d = np.genfromtxt(filename)
# get the last column
y = d.T[-1]
mn = np.mean(y)
# generate a constant column
pred = np.ones(len(y)) * mn
# calculate RMS error
err = SRFF.rmse(y, pred)
print("Predicting a constant %f gets error %f" % (mn, err))
def predict_linear_regression(filename):
from sklearn.linear_model import LinearRegression
d = np.genfromtxt(filename)
# split into X (all columns up to last) and y (last column)
X = d.T[:-1].T
y = d.T[-1:].T
lr = LinearRegression()
lr.fit(X, y)
pred = lr.predict(X)
err = SRFF.rmse(pred.T[0], y.T[0])
print("Linear Regression gets error %f" % (err,))