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