Instead of combining with Kirby for joint attacks, Gooey is playable on his own - a second player can pick up a second controller and take command of him at any time. Like the six animal helpers before him, Gooey could appear to help Kirby out - but, uniquely, he was his own man. Another new friend introduced in Dream Land 3 was Gooey. The variety of different powers and combinations means lot of new abilities to discover - so even though the standard side-scrolling stages are simple to get through, you can get through them with your own choice of technique. Join up with Chuchu, and the umbrella will turn into a spinning top which both she and Kirby will ride around in circles. But pair that Parasol with Nago, and you'll discover a pogo-stick jumping power straight out of NES DuckTales. Kirby with the Parasol power by himself will swing an umbrella like a sword, and drift slowly through the air. Each of the six can be paired up with our hero to access combined abilities - Kirby has his standard power to suck up and absorb his enemies' powers, of course, but if he has an enemy power plus an active animal friend at the same time, the results are more destructive (and comical). Kirby's+old+animal+friends+like+Rick+the+Hamster+(right)meet+up+with+new+buddies+like+Nago+the+Cat+(left). The characters include Rick the Hamster, Coo the Owl and Kine the Fish from DL2, along with newcomers Nago the Cat, Pitch the Bird and Chuchu the Pink Blob-Like Thing. These Yoshi-like partner characters made their debut in the Game Boy's Kirby's Dream Land 2, and continue in their supportive role in this sequel (and in greater numbers). And, if they do take the 800 Wii Point plunge, they should be suitably satisfied - because while Kirby's Dream Land 3 isn't the most dynamic and memorable of the pink puffball's adventures, it does have a variety of unique ideas that set it apart. But things might be different in 2009.īecause so many players missed out on it when it first arrived in cartridge form, there should be a fairly large crowd of fans who might now consider investing eight bucks to own this under-the-radar Kirby outing on the Virtual Console. Yes, the timing of its original release did Kirby's Dream Land 3 no favors at all, back in 1997. Comparison to a superior predecessor also damaged its appeal, as the absolutely superb Kirby Super Star had just arrived the year before as well - and that game set a bar that Kirby games since are still trying to beat. Released on the 16-bit SNES over a full year after the debut of the next-generation Nintendo 64, its potential audience had been considerably reduced by hardware migration. Here's another game to which time was unkind.
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Y: A numpy array or pandas series/dataframe of the target variable of the linear regression model X: A numpy array or pandas dataframe of the features to use in building the linear regression model plot ( x, y, label = label, lw = 1, ls = '-', color = 'red' ) def diagnostic_plots ( X, y, model_fit = None ): """įunction to reproduce the 4 base plots of an OLS model in R. Helper function for plotting cook's distance lines Wrapping it all in a function 1ĭef graph ( formula, x_range, label = None ): """ Below, I provide the code for the function to reproduce the plots in Python. This was something I had initially set out to do myself but did not find much success. Lastly, there will be readers who after seeing this post will want to reproduce these plots in a systematic way. Furthermore, I showed various ways to interpret them using a sample dataset. In this post I set out to reproduce, using Python, the diagnostic plots found in the R programming language. If the point is removed, we would re-run this analysis again and determine how much the model improved. I would argue that removing the point on the far right of the plot should improve the model. In practice, there may be cases where we may want to remove points with a Cook’s distance of less than 0.5, especially if there are only a few observations compared to the rest of the data. In this plot, we do not have any leverage points that meet this criteria. Thanks to Cook’s Distance, we only need to find leverage points that have a distance greater than 0.5. annotate ( i, xy = ( model_leverage, model_norm_residuals )) įortunately, this arguably one of the easiest plots to interpret. argsort ( model_cooks ), 0 ) for i in leverage_top_3 : plot_lm_4. set_ylabel ( 'Standardized Residuals' ) # annotations leverage_top_3 = np. set_title ( 'Residuals vs Leverage' ) plot_lm_4. set_xlim ( 0, max ( model_leverage ) + 0.01 ) plot_lm_4. columns, data = dataframe, lowess = True, scatter_kws = ) plot_lm_4. hat_matrix_diag # cook's distance, from statsmodels internals model_cooks = model_fit. abs ( model_residuals ) # leverage, from statsmodels internals model_leverage = model_fit. abs ( model_norm_residuals )) # absolute residuals model_abs_resid = np. resid_studentized_internal # absolute squared normalized residuals model_norm_residuals_abs_sqrt = np. resid # normalized residuals model_norm_residuals = model_fit. fittedvalues # model residuals model_residuals = model_fit. # model values model_fitted_y = model_fit. Let’s look at an example in R, and its corresponding output, using the Boston housing data. These 4 plots examine a few different assumptions about the model and the data:ġ) The data can be fit by a line (this includes any transformations made to the predictors, e.g., or )Ģ) Errors are normally distributed with mean zeroģ) Errors have constant variance, i.e., homoscedasticity Each of these plots will focus on the residuals - or errors - of a model, which is mathematical jargon for the difference between the actual value and the predicted value, i.e. We will be looking at four main plots in this post and describe how each of them can be used to diagnose issues in an OLS model. In short, diagnostic plots help us determine visually how our model is fitting the data and if any of the basic assumptions of an OLS model are being violated. This post will leverage a lot of that work and at the end will wrap it all in a function that anyone can cut and paste into their code to reproduce these plots regardless of the dataset. So, I did what most people in my situation would do - I turned to Google for help.Īfter trying different queries, I eventually found this excellent resource that was helpful in recreating these plots in a programmatic way. From using R, I had familiarized myself with debugging and tweaking OLS models with the built-in diagnostic plots, but after switching to Python I didn’t know how to get the original plots from R that I had turned to time and time again. Making the switch to Python after having used R for several years, I noticed there was a lack of good base plots for evaluating ordinary least squares (OLS) regression models in Python. If you're sick of wussy games and you want to be tested, play this. I can't imagine how ridiculous that would be in practice, but I bet it's pretty awesome when it works, never mind awesome looking. There's a cooperative mode where multiple players can each take command of a couple of the Commandos as well. Don't even try to remember what the pistol key is, you're just dead. You stray into a green triangle, you're dead. If I gave this level a proper shot, I'm sure that would change quickly because nothing funny happens when you mess up. I'm still at the point where failure is amusing rather than frustrating. If you could refine the keys to something regular like QWERTY for Knife, Pistol, Hand, Movement, Special Action, Special Weapon, it would be a lot easier to remember. On top of that, you've got the F Keys if you want to have split screen (you can set up these views to automatically follow patrols), numpad plus-minus to zoom in and out and Ctrl-S and Ctrl-L to quick save and load. The keyboard shortcuts are all over the place pretty much every key on the keyboard is assigned to a single character's unique action. Moving the mouse back and forth to the backpack icons is clumsy, and hitting their irregular shapes in a hurry is error-prone. It sets up these vast scenes, then steps back and asks 'You think you've got a plan that'll get everybody though this mission without a scratch? I'd really love to see that. It doesn't give you advice or do anything for you. I'm going to be here forever!Ĭommandos doesn't screw around. If I pull this off, it'll be the heist of the century. The entire level is like an intricate clockwork machine. There's plenty of soldiers on both sides of the river, and there's trucks coming and going on the far side. I can turn 'em off, but where'd the fun be in that?Īnd all of this is on the opposite side of a river. These character voices are great, the Marine's got a really sarky voice. Unlike the knife, you don't have to be right next to the person to use it, which means you don't have to watch your guy chase his mark for five seconds every time. The Marine's harpoon (J key) is really useful. (Back in the old days, we had QAOP and it sucked.) That's the name of the alien who came up with that arrangement. The key combinations for the Green Beret are: X to knife, Q to drop radio, I to toggle radio, G for pistol and H for hand. Unfortunately, the keys a little random and mostly different for each character. As everything happens so fast in this game, I'd wager it becomes impossible to play any other way. With these, I can react much faster and with far greater precision. I've written some down so I don't make any more stupid mistakes like in the tutorial. There's a help system in the game telling you what all the characters' abilities do and what the (fixed) key shortcuts are. He's not that guy who's conked out next to the boat. 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