def main():
print("main started")
samples, labels = load_trainingset()
# Build a model using a class, and after the compilation we'll start the training
model = LinearModel("my_model")
model.compile_model()
model.training(samples, labels)
# Evaluate a sample using the model
print(model.predict([30.0]))
print("main finish")
from model_evaluation import r_squared
data = pd.read_csv("boston_house_price.txt", sep='|')
data = data[["LSTAT", "RM", "PTRATIO", "INDUS", "MEDV"]].values
data = (data - np.min(data)) / (np.max(data) - np.min(data))
N = data.shape[0]
RSQ = []
for step in range(1):
np.random.shuffle(data)
train = data[:int(N * 0.7), :]
test = data[int(N * 0.7):, :]
X_train = train[:, :-1]
Y_train = train[:, [-1]]
X_test = test[:, :-1]
Y_test = test[:, [-1]]
n_feat = X_train.shape[1]
model = LinearModel(n_feat)
model.training(X_train, Y_train, alpha=1.0, eps=0.001)
hatY_train = model.predict(X_train)
hatY_test = model.predict(X_test)
r1 = r_squared(Y_train, hatY_train)
r2 = r_squared(Y_test, hatY_test)
print(r1, r2)
sys.stdout.flush()
