In this game, the player scores points based on the face values of the dice, and also on some decisions the player has to make during the game. The game consists of six rounds. During each round, the player will roll some or all of their five dice three times. At the conclusion of each round, the player must choose how their dice are to be scored by choosing which die value to score. Each matching die is worth the number of points shown on its face. For instance, if the dice values are 5, 3, 2, 3, 4, and the player chooses to score the 3s, the player will earn six points (0 + 3 + 0 + 3 + 0 = 6). What makes the game tricky is that the player must choose how to score one round’s set of dice before starting the next round, and they can’t change this choice later. If the player scored the 3s from Round 1, and Round 2’s dice are 3, 3, 3, 2, 3, the player can not elect to score the 3s from this round. However, the player may choose to help themselves out during a round by choosing to “keep” a particular value after a roll. For instance, if the player has already scored the 3s in Round 1, and Round 2 starts with 3, 2, 5, 3, 5, then the player may choose to keep the 5s and re-roll only the other dice, since the 3s won’t be any help. Please note that the player may only choose a value to keep, and not a collection of dice with different values. At the end of the game, the sum of the six rounds’ scores is the player’s final score. This game is generally played with two players and five actual dice; in this game, you will simulate a one-player version.
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So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...
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