print(Ytrain.shape)
print(" ")
print("Seed: {}".format(np.random.get_state()[1][0]))
#k=50000
#print("Selecting {} samples from training set.".format(k))
#Xtrain=Xtrain[0:k-1,:]
#Ytrain=Ytrain[0:k-1]
print(" ")
print("-------------------------------------")
print("Neural Network: ")
print(" ")
print(" ")
clf = nn_1(len(Xtrain[0, :]))
clf.load_weights("jacob_baseline.hdf5")
#clf = load_model("jacob_baseline.hdf5")
print(" ")
print(" ")
reg_evaluate_model()
print("-------------------------------------")
print("Linear Regression")
clf = linear_model.LinearRegression(n_jobs=4)
clf.fit(Xtrain, Ytrain)
reg_evaluate_model()
print("-------------------------------------")
print("Ridge Regression")
clf = linear_model.Ridge()
clf.fit(Xtrain, Ytrain)
