![]() ![]() ![]() Of course, if you want to stick with a slab phone, we have a guide on the best Android phones to consider too. We've tested every foldable released so far, and here’s our in-depth breakdown of what each foldable has to offer. If you're interested in trying now or considering it down the line, we here at XDA are here to help. The point is, while 2022 has given us some excellent slab smartphones, it is perhaps foldable phones that are most exciting. Granted, all but Motorola’s and the Huawei Mate XS 2 are officially sold in China only, but importing is possible. Honor's Magic Vs manages to cram a large 5,000 mAh battery into a body thinner than Samsung's Galaxy Z Fold 4. Vivo managed to cram not one but two in-display fingerprint readers into each screen. Xiaomi's latest Mix Fold 2, for example, is thinner than any previous large screen inner foldable before it. And these brands have made breakthroughs of their own. This year Samsung has further refined the fourth generation of the Fold and Flip, and the results are two awesome mainstream-ready foldables that are proof that foldables are no fad - they are indeed the future of the mobile form factor.īut Samsung’s not the only one making foldables - Xiaomi, Vivo, Oppo, and even an obscure brand named Royole have functional foldables out on the market now. Samsung has taken significant steps in addressing those concerns in the last couple of years, first by adding an official IP water resistance rating to the Galaxy Z Fold 3 and Flip 3, while lowering prices for both devices to boot. ![]() The idea of a foldable smartphone has seen its fair share of supporters and detractors, with the latter group mostly bemoaning their high prices and supposed fragility. More foldables are coming soon – Here's what to expect.Most Obscure Foldable Phone for Collectors: Royole FlexPai 2.Also great value Foldable Phone: Xiaomi Mix Fold 2.Best Value Foldable Phone: Samsung Galaxy Z Fold 3.Foldable Phone with the Best Cameras: Huawei Mate X2.Best Nostalgic Foldable: Motorola Razr 2022.Also Great Compact Foldable Phone: Oppo Find N2.Best Compact Foldable Phone: Samsung Galaxy Z Flip 4.Also Great Overall Foldable: Vivo X Fold.Best Overall Foldable: Samsung Galaxy Z Fold 4.Group labels for the samples used while splitting the dataset into groups array-like of shape (n_samples,), default=None The target variable for supervised learning problems. y array-like of shape (n_samples,), default=None Training data, where n_samples is the number of samplesĪnd n_features is the number of features. Parameters : X array-like of shape (n_samples, n_features) Generate indices to split data into training and test set. Returns the number of splitting iterations in the cross-validator. ![]() groups objectĪlways ignored, exists for compatibility. y objectĪlways ignored, exists for compatibility. Returns the number of splitting iterations in the cross-validator Parameters : X objectĪlways ignored, exists for compatibility. Get_n_splits ( X = None, y = None, groups = None ) ¶ Returns the number of splitting iterations in the cross-validator print ( f "Fold " ) Fold 0: Train: index= Test: index= Fold 1: Train: index= Test: index= get_n_splits ( X ) 2 > print ( kf ) KFold(n_splits=2, random_state=None, shuffle=False) > for i, ( train_index, test_index ) in enumerate ( kf. array () > kf = KFold ( n_splits = 2 ) > kf. import numpy as np > from sklearn.model_selection import KFold > X = np. ![]()
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