def result(self, home, away): #calculate the result for current season here
new = MLR()
new.calculate_points(home, away)
new.count_h2h(home, away)
new.current_standings(home, away)
new.current_season_games(home, away)
new.last_4_games_this_season(home,away)
new.X()
self.X = new.x[0]
self.X = np.matrix(self.X)
# check if x-values lie between the limits of the training set
for i in xrange(13):
if self.X[0,i] > self.x_max[i]:
self.X[0,i] = self.x_max[i]
if self.X[0,i] < self.x_min[i]:
self.X[0,i] = self.x_min[i]
self.X = np.concatenate((self.X,np.matrix('1')),axis = 1)
return
def result(self, home,
away): #calculate the result for current season here
new = MLR()
new.calculate_points(home, away)
new.count_h2h(home, away)
new.current_standings(home, away)
new.current_season_games(home, away)
new.last_4_games_this_season(home, away)
new.X()
self.X = new.x[0]
self.X = np.matrix(self.X)
# check if x-values lie between the limits of the training set
for i in xrange(13):
if self.X[0, i] > self.x_max[i]:
self.X[0, i] = self.x_max[i]
if self.X[0, i] < self.x_min[i]:
self.X[0, i] = self.x_min[i]
self.X = np.concatenate((self.X, np.matrix('1')), axis=1)
return
