@@ -0,0 +1,1302 @@
+import numpy as np%0Aimport model_1%0A%0Adef evaluate(inp, probs):%0A    num_players = list(inp%5B0,:4%5D).index(1) + 4%0A    most_probable_action = list(probs).index(max(list(probs)))%0A%0A    num_correct = 0%0A    log_prob_sum = 0%0A    num_total = 0%0A    turn = 0%0A    folded = %5BFalse%5D * num_players%0A    for action in inp:%0A        if max(list(action%5B4:%5D)) == 0:%0A            continue%0A        action = list(action%5B4:%5D).index(1)%0A        if action != 4 and action != 3:%0A            if action == most_probable_action:%0A                num_correct += 1%0A            log_prob_sum -= np.log(probs%5Baction%5D)%0A        else:%0A            num_correct += 1%0A%0A        num_total += 1%0A        turn = (turn + 1) %25 num_players%0A%0A    return (num_total, num_correct, log_prob_sum)%0A%0Aif __name__ == '__main__':%0A    X_train, y_train, X_test, y_test = model_1.load_training_data()%0A    probs = np.mean(y_train, (0,1))%5B0:3%5D%0A    probs = np.multiply(probs, 1.0 / np.sum(probs))%0A    print probs%0A    num_total = 0;%0A    num_correct = 0;%0A    log_prob_sum = 0;%0A    for hand in X_train:%0A        num_total_incr, num_correct_incr, log_prob_sum_incr = evaluate(hand, probs)%0A        num_total += num_total_incr%0A        num_correct += num_correct_incr%0A        log_prob_sum += log_prob_sum_incr%0A    print float(num_correct) / num_total%0A    print log_prob_sum / num_total

Add dummy model.